{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:04:55Z","timestamp":1776132295309,"version":"3.50.1"},"reference-count":284,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["62476109, 62206108, 62225602, and 62176184"],"award-info":[{"award-number":["62476109, 62206108, 62225602, and 62176184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Research Foundation Singapore and DSO National Laboratories under the AI Singapore Programme","award":["AISG4-GC-2023-010-1B"],"award-info":[{"award-number":["AISG4-GC-2023-010-1B"]}]},{"name":"The Fundamental Research Funds for the Central Universities; and the Shanghai Central Guidance Local Science and Technology Development Fund Project","award":["YDZX20253100002004"],"award-info":[{"award-number":["YDZX20253100002004"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain significant. A fundamental objective of AI research is to achieve robust generalization with limited-resource data. This survey employs agnostic active sampling theory within the Probably Approximately Correct (PAC) framework to analyze the generalization error and label complexity associated with learning from low-resource data in both model-agnostic supervised and unsupervised settings. Based on this analysis, we investigate a suite of optimization strategies tailored for low-resource data learning, including gradient-informed optimization, meta-iteration optimization, geometry-aware optimization, and LLMs-powered optimization. Furthermore, we provide a comprehensive overview of multiple learning paradigms that can benefit from low-resource data, including domain transfer, reinforcement feedback, and hierarchical structure modeling. Finally, we conclude our analysis and investigation by summarizing the key findings and highlighting their implications for learning with low-resource data.<\/jats:p>","DOI":"10.1145\/3773075","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T11:20:28Z","timestamp":1761304828000},"page":"1-47","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-7391-0334","authenticated-orcid":false,"given":"Xiaofeng","family":"Cao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Tongji University","place":["Shanghai, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0009-0001-0805-9002","authenticated-orcid":false,"given":"Mingwei","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University","place":["Changchun, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-0269-5649","authenticated-orcid":false,"given":"Xin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The University of Queensland","place":["Brisbane, Australia"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-6115-5194","authenticated-orcid":false,"given":"Jiangchao","family":"Yao","sequence":"additional","affiliation":[{"name":"Cooperative Medianet Innovation Center, Shanghai Jiao Tong University","place":["Shanghai, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-3784-7788","authenticated-orcid":false,"given":"Wei","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Tongji University","place":["Shanghai, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-7673-5367","authenticated-orcid":false,"given":"Shengjun","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-1880-5918","authenticated-orcid":false,"given":"Minling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-8095-4637","authenticated-orcid":false,"given":"Ivor","family":"Tsang","sequence":"additional","affiliation":[{"name":"Australian Artificial Intelligence Institute, University of Technology Sydney","place":["Sydney, Australia"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-4480-169X","authenticated-orcid":false,"given":"Yew-Soon","family":"Ong","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Nanyang Technological University","place":["Singapore, Singapore"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-4828-8248","authenticated-orcid":false,"given":"James T.","family":"Kwok","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology","place":["Hong Kong, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-2999-2088","authenticated-orcid":false,"given":"Heng Tao","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technolog, Tongji University","place":["Shanghai, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"e_1_3_4_2_2","first-page":"577","volume-title":"Proceedings of the 41st International Conference on Machine Learning","volume":"235","author":"Ahmaditeshnizi Ali","year":"2024","unstructured":"Ali Ahmaditeshnizi, Wenzhi Gao, and Madeleine Udell. 2024. OptiMUS: Scalable optimization modeling with (MI)LP Solvers and Large Language Models. In Proceedings of the 41st International Conference on Machine Learning, Vol. 235. PMLR, 577\u2013596."},{"key":"e_1_3_4_3_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Alet* Ferran","year":"2020","unstructured":"Ferran Alet*, Martin F. Schneider*, Tomas Lozano-Perez, and Leslie Pack Kaelbling. 2020. Meta-learning curiosity algorithms. In Proceedings of the International Conference on Learning Representations."},{"issue":"10","key":"e_1_3_4_4_2","doi-asserted-by":"crossref","first-page":"10804","DOI":"10.1609\/aaai.v38i10.28953","article-title":"Sample efficient reinforcement learning with partial dynamics knowledge","volume":"38","author":"Alharbi Meshal","year":"2024","unstructured":"Meshal Alharbi, Mardavij Roozbehani, and Munther Dahleh. 2024. Sample efficient reinforcement learning with partial dynamics knowledge. Proceedings of the AAAI Conference on Artificial Intelligence 38, 10 (2024), 10804\u201310811.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"2","key":"e_1_3_4_5_2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10994-009-5103-0","article-title":"NP-hardness of euclidean sum-of-squares clustering","volume":"75","author":"Aloise Daniel","year":"2009","unstructured":"Daniel Aloise, Amit Deshpande, Pierre Hansen, and Preyas Popat. 2009. NP-hardness of euclidean sum-of-squares clustering. Machine Learning 75, 2 (2009), 245\u2013248.","journal-title":"Machine Learning"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","unstructured":"Hans Wilhelm Alt. 2016. Linear functional analysis: An application-oriented introduction (1st ed.). Springer London. DOI:10.1007\/978-1-4471-7280-2","DOI":"10.1007\/978-1-4471-7280-2"},{"key":"e_1_3_4_7_2","unstructured":"Raviteja Anantha Stephen Pulman and Srinivas Chappidi. 2020. Generalized reinforcement meta learning for few-shot optimization. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2005.01246"},{"key":"e_1_3_4_8_2","first-page":"5055","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Andrychowicz Marcin","year":"2017","unstructured":"Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba. 2017. Hindsight experience replay. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 5055\u20135065."},{"key":"e_1_3_4_9_2","unstructured":"Rohan Anil Andrew M. Dai Orhan Firat Melvin Johnson Dmitry Lepikhin Alexandre Passos Siamak Shakeri Emanuel Taropa Paige Bailey Zhifeng Chen and others. 2023. PaLM 2 Technical Report. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2305.10403"},{"key":"e_1_3_4_10_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Badia Adri\u00e0 Puigdom\u00e8nech","year":"2020","unstructured":"Adri\u00e0 Puigdom\u00e8nech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martin Arjovsky, Alexander Pritzel, Andrew Bolt, et\u00a0al. 2020. Never give up: Learning directed exploration strategies. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_11_2","first-page":"1006","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Balaji Yogesh","year":"2018","unstructured":"Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. 2018. MetaReg: Towards domain generalization using meta-regularization. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 1006\u20131016."},{"issue":"6018","key":"e_1_3_4_12_2","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1126\/science.1197448","article-title":"More is less: Signal processing and the data deluge","volume":"331","author":"Baraniuk Richard G.","year":"2011","unstructured":"Richard G. Baraniuk. 2011. More is less: Signal processing and the data deluge. Science 331, 6018 (2011), 717\u2013719.","journal-title":"Science"},{"key":"e_1_3_4_13_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Bellemare Marc","year":"2016","unstructured":"Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos. 2016. Unifying count-based exploration and intrinsic motivation. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_14_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Bertinetto Luca","year":"2016","unstructured":"Luca Bertinetto, Jo\u00e3o F. Henriques, Jack Valmadre, Philip Torr, and Andrea Vedaldi. 2016. Learning feed-forward one-shot learners. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_15_2","first-page":"49","volume-title":"Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009","author":"Beygelzimer Alina","year":"2009","unstructured":"Alina Beygelzimer, Sanjoy Dasgupta, and John Langford. 2009. Importance weighted active learning. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009. 49\u201356."},{"key":"e_1_3_4_16_2","volume-title":"Proceedings of the NeurIPS 2024 Workshop on Open-World Agents","author":"Bhambri Siddhant","year":"2024","unstructured":"Siddhant Bhambri, Amrita Bhattacharjee, huan liu, and Subbarao Kambhampati. 2024. Efficient reinforcement learning via large language model-based search. In Proceedings of the NeurIPS 2024 Workshop on Open-World Agents."},{"key":"e_1_3_4_17_2","unstructured":"Xiao Bi Deli Chen Guanting Chen Shanhuang Chen Damai Dai Chengqi Deng Honghui Ding Kai Dong Qiushi Du Zhe Fu and others. 2024. DeepSeek LLM: Scaling open-source language models with longtermism. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2401.02954"},{"key":"e_1_3_4_18_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Bjorck Nils","year":"2018","unstructured":"Nils Bjorck, Carla P. Gomes, Bart Selman, and Kilian Q. Weinberger. 2018. Understanding batch normalization. In Proceedings of the Advances in Neural Information Processing Systems."},{"issue":"2","key":"e_1_3_4_19_2","first-page":"1","article-title":"Domain generalization by marginal transfer learning","volume":"22","author":"Blanchard Gilles","year":"2021","unstructured":"Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott. 2021. Domain generalization by marginal transfer learning. Journal of Machine Learning Research 22, 2 (2021), 1\u201355.","journal-title":"Journal of Machine Learning Research"},{"issue":"518","key":"e_1_3_4_20_2","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","article-title":"Variational inference: A review for statisticians","volume":"112","author":"Blei David M.","year":"2017","unstructured":"David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe. 2017. Variational inference: A review for statisticians. Journal of the American Statistical Association 112, 518 (2017), 859\u2013877.","journal-title":"Journal of the American Statistical Association"},{"issue":"4","key":"e_1_3_4_21_2","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1145\/76359.76371","article-title":"Learnability and the Vapnik-Chervonenkis dimension","volume":"36","author":"Blumer Anselm","year":"1989","unstructured":"Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth. 1989. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM 36, 4 (1989), 929\u2013965.","journal-title":"Journal of the ACM"},{"key":"e_1_3_4_22_2","doi-asserted-by":"crossref","unstructured":"Nicolas Boumal. 2023. An Introduction to Optimization on Smooth Manifolds. Cambridge University Press Cambridge.","DOI":"10.1017\/9781009166164"},{"key":"e_1_3_4_23_2","volume-title":"Bayesian Inference in Statistical Analysis","author":"Box George E. P.","year":"2011","unstructured":"George E. P. Box and George C. Tiao. 2011. Bayesian Inference in Statistical Analysis. John Wiley and Sons."},{"key":"e_1_3_4_24_2","doi-asserted-by":"crossref","unstructured":"Shuvayan Brahmachary Subodh M. Joshi Aniruddha Panda Kaushik Koneripalli Arun Kumar Sagotra Harshil Patel Ankush Sharma Ameya D. Jagtap and Kaushic Kalyanaraman. 2025. Large language model-based evolutionary optimizer: Reasoning with elitism. Neurocomputing 622 (2025) 129272.","DOI":"10.1016\/j.neucom.2024.129272"},{"key":"e_1_3_4_25_2","first-page":"1877","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et\u00a0al. 2020. Language models are few-shot learners. In Proceedings of the Advances in Neural Information Processing Systems. 1877\u20131901."},{"key":"e_1_3_4_26_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Buckman Jacob","year":"2018","unstructured":"Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee. 2018. Sample-efficient reinforcement learning with stochastic ensemble value expansion. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_27_2","unstructured":"Yuri Burda Harrison Edwards Amos Storkey and Oleg Klimov. 2019. Exploration by random network distillation. In International Conference on Learning Representations. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/openreview.net\/forum?id=H1lJJnR5Ym"},{"issue":"1","key":"e_1_3_4_28_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0004-3702(01)00129-1","article-title":"Deep blue","volume":"134","author":"Campbell Murray","year":"2002","unstructured":"Murray Campbell, A. Joseph Hoane Jr, and Feng-hsiung Hsu. 2002. Deep blue. Artificial Intelligence 134, 1-2(2002), 57\u201383.","journal-title":"Artificial Intelligence"},{"issue":"11","key":"e_1_3_4_29_2","first-page":"13422","article-title":"Data-Efficient Learning via Minimizing Hyperspherical Energy","volume":"45","author":"Cao Xiaofeng","year":"2023","unstructured":"Xiaofeng Cao, Weiyang Liu, and Ivor W. Tsang. 2023. Data-Efficient Learning via Minimizing Hyperspherical Energy. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 11 (2023), 13422\u201313437.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"10","key":"e_1_3_4_30_2","doi-asserted-by":"crossref","first-page":"6872","DOI":"10.1109\/TPAMI.2021.3093590","article-title":"Distribution disagreement via lorentzian focal representation","volume":"44","author":"Cao Xiaofeng","year":"2022","unstructured":"Xiaofeng Cao and Ivor W. Tsang. 2022. Distribution disagreement via lorentzian focal representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 10 (2022), 6872\u20136889.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"e_1_3_4_31_2","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TNNLS.2020.3027605","article-title":"Shattering distribution for active learning","volume":"33","author":"Cao Xiaofeng","year":"2022","unstructured":"Xiaofeng Cao and Ivor W. Tsang. 2022. Shattering distribution for active learning. IEEE Transactions on Neural Networks and Learning Systems 33, 1 (2022), 215\u2013228.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"1","key":"e_1_3_4_32_2","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TIT.2007.911292","article-title":"Improved risk tail bounds for on-line algorithms","volume":"54","author":"Cesa-Bianchi Nicolo","year":"2008","unstructured":"Nicolo Cesa-Bianchi and Claudio Gentile. 2008. Improved risk tail bounds for on-line algorithms. IEEE Transactions on Information Theory 54, 1 (2008), 386\u2013390.","journal-title":"IEEE Transactions on Information Theory"},{"key":"e_1_3_4_33_2","first-page":"4868","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Chami Ines","year":"2019","unstructured":"Ines Chami, Zhitao Ying, Christopher R\u00e9, and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems. 4868\u20134879."},{"key":"e_1_3_4_34_2","doi-asserted-by":"crossref","first-page":"7346","DOI":"10.1109\/CVPR.2019.00753","volume-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Chang Woong-Gi","year":"2019","unstructured":"Woong-Gi Chang, Tackgeun You, Seonguk Seo, Suha Kwak, and Bohyung Han. 2019. Domain-specific batch normalization for unsupervised domain adaptation. In Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7346\u20137354."},{"key":"e_1_3_4_35_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Chauhan Jatin","year":"2020","unstructured":"Jatin Chauhan, Deepak Nathani, and Manohar Kaul. 2020. Few-shot learning on graphs via super-classes based on graph spectral measures. In Proceedings of the International Conference on Learning Representations."},{"issue":"1","key":"e_1_3_4_36_2","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TCSVT.2021.3058098","article-title":"Hierarchical Graph Neural Networks for Few-Shot Learning","volume":"32","author":"Chen Cen","year":"2022","unstructured":"Cen Chen, Kenli Li, Wei Wei, Joey Tianyi Zhou, and Zeng Zeng. 2022. Hierarchical Graph Neural Networks for Few-Shot Learning. IEEE Transactions on Circuits and Systems for Video Technology 32, 1 (2022), 240\u2013252.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_4_37_2","doi-asserted-by":"crossref","first-page":"6592","DOI":"10.1109\/CVPR46437.2021.00653","volume-title":"Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Chen Chaofan","year":"2021","unstructured":"Chaofan Chen, Xiaoshan Yang, Changsheng Xu, Xuhui Huang, and Zhe Ma. 2021. ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning. In Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 6592\u20136601."},{"key":"e_1_3_4_38_2","first-page":"1551","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","author":"Chen Hao","year":"2024","unstructured":"Hao Chen, Ran Tao, Han Zhang, Yidong Wang, Xiang Li, Wei Ye, Jindong Wang, Guosheng Hu, and Marios Savvides. 2024. Conv-adapter: Exploring parameter efficient transfer learning for ConvNets. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 1551\u20131561."},{"key":"e_1_3_4_39_2","first-page":"7683","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Chen Jin","year":"2023","unstructured":"Jin Chen, Zhi Gao, Xinxiao Wu, and Jiebo Luo. 2023. Meta-causal learning for single domain generalization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7683\u20137692."},{"key":"e_1_3_4_40_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast learning with graph convolutional networks via importance sampling. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_41_2","first-page":"7809","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Chen Runjin","year":"2024","unstructured":"Runjin Chen, Tong Zhao, Ajay Kumar Jaiswal, Neil Shah, and Zhangyang Wang. 2024. LLaGA: Large language and graph assistant. In Proceedings of the 41st International Conference on Machine Learning. 7809\u20137823."},{"key":"e_1_3_4_42_2","first-page":"1594","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Shixiang","year":"2021","unstructured":"Shixiang Chen, Alfredo Garcia, Mingyi Hong, and Shahin Shahrampour. 2021. Decentralized riemannian gradient descent on the stiefel manifold. In Proceedings of the International Conference on Machine Learning. PMLR, 1594\u20131605."},{"key":"e_1_3_4_43_2","first-page":"1695","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Chen Tianlong","year":"2021","unstructured":"Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang. 2021. A unified lottery ticket hypothesis for graph neural networks. In Proceedings of the 38th International Conference on Machine Learning. 1695\u20131706."},{"key":"e_1_3_4_44_2","first-page":"2580","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Chen Vincent S.","year":"2019","unstructured":"Vincent S. Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Re, and Li Fei-Fei. 2019. Scene graph prediction with limited labels. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2580\u20132590."},{"key":"e_1_3_4_45_2","first-page":"4639","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Chen Xianing","year":"2025","unstructured":"Xianing Chen, Si Huo, Borui Jiang, Hailin Hu, and Xinghao Chen. 2025. Single domain generalization for few-shot counting via universal representation matching. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4639\u20134649."},{"key":"e_1_3_4_46_2","first-page":"2278","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI-21","author":"Chen Yuzhao","year":"2021","unstructured":"Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, and Junzhou Huang. 2021. On self-distilling graph neural network. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI-21. 2278\u20132284."},{"key":"e_1_3_4_47_2","first-page":"20970","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Chi Haoang","year":"2021","unstructured":"Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William Cheung, and James Kwok. 2021. TOHAN: A one-step approach towards few-shot hypothesis adaptation. In Proceedings of the Advances in Neural Information Processing Systems. 20970\u201320982."},{"key":"e_1_3_4_48_2","first-page":"257","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chiang Wei-Lin","year":"2019","unstructured":"Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 257\u2013266."},{"key":"e_1_3_4_49_2","first-page":"1","volume-title":"Proceedings of the EMNLP","author":"Choi Juhwan","year":"2024","unstructured":"Juhwan Choi, Yeonghwa Kim, Seunguk Yu, Jungmin Yun, and Youngbin Kim. 2024. UniGen: Universal domain generalization for sentiment classification via zero-shot dataset generation. In Proceedings of the EMNLP. 1\u201314."},{"key":"e_1_3_4_50_2","first-page":"4759","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Chua Kurtland","year":"2018","unstructured":"Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine. 2018. Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 4759\u20134770."},{"key":"e_1_3_4_51_2","first-page":"2606","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chwialkowski Kacper","year":"2016","unstructured":"Kacper Chwialkowski, Heiko Strathmann, and Arthur Gretton. 2016. A kernel test of goodness of fit. In Proceedings of the International Conference on Machine Learning. PMLR, 2606\u20132615."},{"issue":"2","key":"e_1_3_4_52_2","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1023\/A:1022673506211","article-title":"Improving generalization with active learning","volume":"15","author":"Cohn David","year":"1994","unstructured":"David Cohn, Les Atlas, and Richard Ladner. 1994. Improving generalization with active learning. Machine Learning 15, 2 (1994), 201\u2013221.","journal-title":"Machine Learning"},{"key":"e_1_3_4_53_2","first-page":"18860","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Collins Liam","year":"2020","unstructured":"Liam Collins, Aryan Mokhtari, and Sanjay Shakkottai. 2020. Task-robust model-agnostic meta-learning. In Proceedings of the Advances in Neural Information Processing Systems. 18860\u201318871."},{"key":"e_1_3_4_54_2","first-page":"2144","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Cortes Corinna","year":"2020","unstructured":"Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, and Ningshan Zhang. 2020. Adaptive region-based active learning. In Proceedings of the International Conference on Machine Learning. PMLR, 2144\u20132153."},{"key":"e_1_3_4_55_2","volume-title":"Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019","author":"Cortes Corinna","year":"2020","unstructured":"Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, and Ningshan Zhang. 2020. Region-based active learning. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019."},{"key":"e_1_3_4_56_2","first-page":"1379","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Cortes Corinna","year":"2019","unstructured":"Corinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, and Claudio Gentile. 2019. Active learning with disagreement graphs. In Proceedings of the International Conference on Machine Learning. PMLR, 1379\u20131387."},{"key":"e_1_3_4_57_2","first-page":"14156","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Curi Sebastian","year":"2020","unstructured":"Sebastian Curi, Felix Berkenkamp, and Andreas Krause. 2020. Efficient model-based reinforcement learning through optimistic policy search and planning. In Proceedings of the Advances in Neural Information Processing Systems. 14156\u201314170."},{"key":"e_1_3_4_58_2","first-page":"2376","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Dance Christopher R.","year":"2021","unstructured":"Christopher R. Dance, Julien Perez, and Th\u00e9o Cachet. 2021. Demonstration-conditioned reinforcement learning for few-shot imitation. In Proceedings of the 38th International Conference on Machine Learning. 2376\u20132387."},{"key":"e_1_3_4_59_2","volume-title":"Proceedings of the 2024 International Joint Conference on Artificial Intelligence","author":"Daoudi Paul","year":"2024","unstructured":"Paul Daoudi, Christophe Prieur, Bogdan Robu, Merwan Barlier, and Ludovic Dos Santos. 2024. A conservative approach for transfer in few-shot off-dynamics reinforcement learning. In Proceedings of the 2024 International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_4_60_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Das Debasmit","year":"2022","unstructured":"Debasmit Das, Sungrack Yun, and Fatih Porikli. 2022. ConFeSS: A framework for single source cross-domain few-shot learning. In Proceedings of the International Conference on Learning Representations."},{"issue":"19","key":"e_1_3_4_61_2","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1016\/j.tcs.2010.12.054","article-title":"Two faces of active learning","volume":"412","author":"Dasgupta Sanjoy","year":"2011","unstructured":"Sanjoy Dasgupta. 2011. Two faces of active learning. Theoretical Computer Science 412, 19 (2011), 1767\u20131781.","journal-title":"Theoretical Computer Science"},{"key":"e_1_3_4_62_2","first-page":"353","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Dasgupta Sanjoy","year":"2008","unstructured":"Sanjoy Dasgupta, Daniel J. Hsu, and Claire Monteleoni. 2008. A general agnostic active learning algorithm. In Proceedings of the Neural Information Processing Systems. 353\u2013360."},{"key":"e_1_3_4_63_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Denevi Giulia","year":"2018","unstructured":"Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, and Massimiliano Pontil. 2018. Learning to learn around a common mean. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_64_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/978-3-031-72784-9_5","volume-title":"Proceedings of the Computer Vision \u2013 ECCV 2024","author":"Diao Haiwen","year":"2025","unstructured":"Haiwen Diao, Bo Wan, Xu Jia, Yunzhi Zhuge, Ying Zhang, Huchuan Lu, and Long Chen. 2025. SHERL: Synthesizing high accuracy and efficient memory for resource-limited transfer learning. In Proceedings of the Computer Vision \u2013 ECCV 2024. 75\u201395."},{"issue":"3","key":"e_1_3_4_65_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.neuron.2012.01.010","article-title":"How does the brain solve visual object recognition?","volume":"73","author":"DiCarlo James J.","year":"2012","unstructured":"James J. DiCarlo, Davide Zoccolan, and Nicole C. Rust. 2012. How does the brain solve visual object recognition? Neuron 73, 3 (2012), 415\u2013434.","journal-title":"Neuron"},{"key":"e_1_3_4_66_2","volume-title":"Proceedings of the AAAI","author":"Ding Kaize","year":"2022","unstructured":"Kaize Ding, Jianling Wang, James Caverlee, and Huan Liu. 2022. Meta propagation networks for graph few-shot semi-supervised learning. In Proceedings of the AAAI."},{"key":"e_1_3_4_67_2","first-page":"295","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Ding Kaize","year":"2020","unstructured":"Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, and Huan Liu. 2020. Graph prototypical networks for few-shot learning on attributed networks. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 295\u2013304."},{"key":"e_1_3_4_68_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"D\u2019Oro Pierluca","year":"2023","unstructured":"Pierluca D\u2019Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, and Aaron Courville. 2023. Sample-efficient reinforcement learning by breaking the replay ratio barrier. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_4_69_2","unstructured":"Mengge Du Yuntian Chen Zhongzheng Wang Longfeng Nie and Dongxiao Zhang. 2024. LLM4ED: Large Language Models for Automatic Equation Discovery. arXiv:2405.07761. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2405.07761"},{"key":"e_1_3_4_70_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Du Yingjun","year":"2021","unstructured":"Yingjun Du, Xiantong Zhen, Ling Shao, and Cees G. M. Snoek. 2021. MetaNorm: Learning to normalize few-shot batches across domains. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_71_2","unstructured":"Yan Duan John Schulman Xi Chen Peter L. Bartlett Ilya Sutskever and Pieter Abbeel. 2016. \\({RL}^{2}\\) : Fast reinforcement learning via slow reinforcement learning. arXiv:1611.02779. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/1611.02779"},{"key":"e_1_3_4_72_2","unstructured":"Adrien Ecoffet Joost Huizinga Joel Lehman Kenneth O. Stanley and Jeff Clune. 2019. Go-explore: A new approach for hard-exploration problems. arXiv:1901.10995. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/1901.10995"},{"key":"e_1_3_4_73_2","unstructured":"Eva Eigner and Thorsten H\u00e4ndler. 2024. Determinants of LLM-assisted Decision-Making. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2402.17385"},{"key":"e_1_3_4_74_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Eysenbach Benjamin","year":"2019","unstructured":"Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. 2019. Diversity is all you need: Learning skills without a reward function. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_75_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1145\/3701716.3715854","volume-title":"Companion Proceedings of the ACM on Web Conference 2025","author":"Fang Yuan","year":"2025","unstructured":"Yuan Fang, Yuxia Wu, Xingtong Yu, and Shirui Pan. 2025. Few-shot learning on graphs: From meta-learning to llm-empowered pre-training and beyond. In Companion Proceedings of the ACM on Web Conference 2025. 9\u201312."},{"key":"e_1_3_4_76_2","first-page":"3542","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Feng Lei","year":"2019","unstructured":"Lei Feng and Bo An. 2019. Partial label learning with self-guided retraining. In Proceedings of the AAAI Conference on Artificial Intelligence. 3542\u20133549."},{"key":"e_1_3_4_77_2","first-page":"1126","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning. PMLR, 1126\u20131135."},{"key":"e_1_3_4_78_2","first-page":"2026","volume-title":"Proceedings of 38th Conference on Learning Theory","author":"Foster Dylan J.","year":"2025","unstructured":"Dylan J. Foster, Zakaria Mhammedi, and Dhruv Rohatgi. 2025. Is a good foundation necessary for efficient reinforcement learning? the computational role of the base model in exploration. In Proceedings of 38th Conference on Learning Theory. 2026\u20132142."},{"key":"e_1_3_4_79_2","first-page":"643","volume-title":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing","author":"Fu Dayuan","year":"2024","unstructured":"Dayuan Fu, Biqing Qi, Yihuai Gao, Che Jiang, Guanting Dong, and Bowen Zhou. 2024. MSI-agent: Incorporating multi-scale insight into embodied agents for superior planning and decision-makin. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 643\u2013659."},{"key":"e_1_3_4_80_2","first-page":"10571","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Gallou\u00e9dec Quentin","year":"2023","unstructured":"Quentin Gallou\u00e9dec and Emmanuel Dellandrea. 2023. Cell-free latent go-explore. In Proceedings of the 40th International Conference on Machine Learning. 10571\u201310586."},{"issue":"1","key":"e_1_3_4_81_2","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. Journal of Machine Learning Research 17, 1(2016), 2096\u20132030.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_4_82_2","volume-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","author":"Gao Chang","year":"2024","unstructured":"Chang Gao, Haiyun Jiang, Deng Cai, Shuming Shi, and Wai Lam. 2024. StrategyLLM: Large language models as strategy generators, executors, optimizers, and evaluators for problem solving. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems."},{"issue":"2","key":"e_1_3_4_83_2","first-page":"1181","article-title":"Dynamic memory-based curiosity: A bootstrap approach for exploration in reinforcement learning","volume":"8","author":"Gao Zijian","year":"2024","unstructured":"Zijian Gao, Yiying Li, Kele Xu, Yuanzhao Zhai, Bo Ding, Dawei Feng, Xinjun Mao, and Huaimin Wang. 2024. Dynamic memory-based curiosity: A bootstrap approach for exploration in reinforcement learning. IEEE Transactions on Emerging Topics in Computational Intelligence 8, 2 (2024), 1181\u20131193.","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"e_1_3_4_84_2","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Gidaris Spyros","year":"2019","unstructured":"Spyros Gidaris and Nikos Komodakis. 2019. Generating classification weights with GNN denoising autoencoders for few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_4_85_2","first-page":"222","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Gong Boqing","year":"2013","unstructured":"Boqing Gong, Kristen Grauman, and Fei Sha. 2013. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In Proceedings of the International Conference on Machine Learning. PMLR, 222\u2013230."},{"key":"e_1_3_4_86_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Gorham Jackson","year":"2015","unstructured":"Jackson Gorham and Lester Mackey. 2015. Measuring sample quality with Stein\u2019s method. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_87_2","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton Arthur","year":"2012","unstructured":"Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Sch\u00f6lkopf, and Alexander Smola. 2012. A kernel two-sample test. Journal of Machine Learning Research 13, Mar(2012), 723\u2013773.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_4_88_2","first-page":"4884","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","author":"Guo Demi","year":"2021","unstructured":"Demi Guo, Alexander Rush, and Yoon Kim. 2021. Parameter-Efficient Transfer Learning with Diff Pruning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 4884\u20134896."},{"key":"e_1_3_4_89_2","unstructured":"Pei-Fu Guo Ying-Hsuan Chen Yun-Da Tsai and Shou-De Lin. 2024. Towards optimizing with large language models. In Fourth Workshop on Knowledge-infused Learning. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/openreview.net\/forum?id=vIU8LUckb4"},{"key":"e_1_3_4_90_2","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"Guo Qingyan","year":"2024","unstructured":"Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. 2024. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. In Proceedings of the 12th International Conference on Learning Representations."},{"key":"e_1_3_4_91_2","unstructured":"Zixian Guo Ming Liu Zhilong Ji Jinfeng Bai Yiwen Guo and Wangmeng Zuo. 2024. LLM as a complementary optimizer to gradient descent: A case study in prompt tuning. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2405.19732"},{"issue":"1","key":"e_1_3_4_92_2","doi-asserted-by":"crossref","first-page":"5721","DOI":"10.1038\/s41467-021-25874-z","article-title":"Embodied intelligence via learning and evolution","volume":"12","author":"Gupta Agrim","year":"2021","unstructured":"Agrim Gupta, Silvio Savarese, Surya Ganguli, and Li Fei-Fei. 2021. Embodied intelligence via learning and evolution. Nature Communications 12, 1 (2021), 5721.","journal-title":"Nature Communications"},{"key":"e_1_3_4_93_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hafner Danijar","year":"2020","unstructured":"Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. 2020. Dream to control: Learning behaviors by latent imagination. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_94_2","first-page":"2555","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Hafner Danijar","year":"2019","unstructured":"Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. 2019. Learning latent dynamics for planning from pixels. In Proceedings of the International Conference on Machine Learning. PMLR, 2555\u20132565."},{"key":"e_1_3_4_95_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hafner Danijar","year":"2021","unstructured":"Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, and Jimmy Ba. 2021. Mastering atari with discrete world models. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_96_2","first-page":"1025","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025\u20131035."},{"key":"e_1_3_4_97_2","unstructured":"William L. Hamilton Rex Ying and Jure Leskovec. 2018. Representation learning on graphs: Methods and applications. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/1709.05584"},{"issue":"1","key":"e_1_3_4_98_2","first-page":"1319","article-title":"The optimal sample complexity of PAC learning","volume":"17","author":"Hanneke Steve","year":"2016","unstructured":"Steve Hanneke. 2016. The optimal sample complexity of PAC learning. The Journal of Machine Learning Research 17, 1 (2016), 1319\u20131333.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_4_99_2","unstructured":"Steve Hanneke. 2016. The optimal sample complexity of PAC learning. The Journal of Machine Learning Research 17 1 (2016) 1319\u20131333."},{"key":"e_1_3_4_100_2","first-page":"1101","volume-title":"Proceedings of the 8th National Conference on Artificial Intelligence-Volume 2","author":"Haussler David","year":"1990","unstructured":"David Haussler. 1990. Probably approximately correct learning. In Proceedings of the 8th National Conference on Artificial Intelligence-Volume 2. AAAI, 1101\u20131108."},{"issue":"9","key":"e_1_3_4_101_2","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He Haibo","year":"2009","unstructured":"Haibo He and Edwardo A. Garcia. 2009. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21, 9 (2009), 1263\u20131284.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_4_102_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"He Junxian","year":"2022","unstructured":"Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2022. Towards a unified view of parameter-efficient transfer learning. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_103_2","doi-asserted-by":"crossref","unstructured":"Michael A. Hedderich Lukas Lange Heike Adel Jannik Str\u00f6tgen and Dietrich Klakow. 2021. A survey on recent approaches for natural language processing in low-resource scenarios. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics 2545\u20132568.","DOI":"10.18653\/v1\/2021.naacl-main.201"},{"key":"e_1_3_4_104_2","first-page":"IV\u2013317","volume-title":"Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP\u201907","author":"Hershey John R.","year":"2007","unstructured":"John R. Hershey and Peder A. Olsen. 2007. Approximating the Kullback Leibler divergence between Gaussian mixture models. In Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP\u201907. IEEE, IV\u2013317."},{"key":"e_1_3_4_105_2","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence","author":"Hester Todd","year":"2018","unstructured":"Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et\u00a0al. 2018. Deep Q-learning from demonstrations. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Article 394, 8 pages."},{"issue":"1","key":"e_1_3_4_106_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1022600917598","article-title":"Generalizing version spaces","volume":"17","author":"Hirsh Haym","year":"1994","unstructured":"Haym Hirsh. 1994. Generalizing version spaces. Machine Learning 17, 1 (1994), 5\u201346.","journal-title":"Machine Learning"},{"key":"e_1_3_4_107_2","unstructured":"Timothy Hospedales Antreas Antoniou Paul Micaelli and Amos Storkey. 2022. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 9 (2022) 5149\u20135169."},{"key":"e_1_3_4_108_2","first-page":"2790","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning. 2790\u20132799."},{"key":"e_1_3_4_109_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hu Edward J.","year":"2022","unstructured":"Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-rank adaptation of large language models. In Proceedings of the International Conference on Learning Representations."},{"issue":"16","key":"e_1_3_4_110_2","doi-asserted-by":"crossref","first-page":"17295","DOI":"10.1609\/aaai.v39i16.33901","article-title":"Large language model meets graph neural network in knowledge distillation","volume":"39","author":"Hu Shengxiang","year":"2025","unstructured":"Shengxiang Hu, Guobing Zou, Song Yang, Shiyi Lin, Yanglan Gan, Bofeng Zhang, and Yixin Chen. 2025. Large language model meets graph neural network in knowledge distillation. Proceedings of the AAAI Conference on Artificial Intelligence 39, 16 (2025), 17295\u201317304.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_4_111_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hu Zhengdong","year":"2022","unstructured":"Zhengdong Hu, Yifan Sun, and Yi Yang. 2022. Switch to generalize: Domain-switch learning for cross-domain few-shot classification. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_112_2","unstructured":"Beichen Huang Xingyu Wu Yu Zhou Jibin Wu Liang Feng Ran Cheng and Kay Chen Tan. 2024. Exploring the true potential: Evaluating the black-box optimization capability of large language models. arXiv:2404.06290. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2404.06290"},{"key":"e_1_3_4_113_2","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1145\/3097983.3098159","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Iosifidis Vasileios","year":"2017","unstructured":"Vasileios Iosifidis and Eirini Ntoutsi. 2017. Large scale sentiment learning with limited labels. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1823\u20131832."},{"key":"e_1_3_4_114_2","first-page":"11719","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Jamal Muhammad Abdullah","year":"2019","unstructured":"Muhammad Abdullah Jamal and Guo-Jun Qi. 2019. Task agnostic meta-learning for few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 11719\u201311727."},{"key":"e_1_3_4_115_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Janner Michael","year":"2019","unstructured":"Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine. 2019. When to trust your model: Model-based policy optimization. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_116_2","first-page":"7073","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Ji Jingwei","year":"2019","unstructured":"Jingwei Ji, Kaidi Cao, and Juan Carlos Niebles. 2019. Learning temporal action proposals with fewer labels. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 7073\u20137082."},{"key":"e_1_3_4_117_2","first-page":"4882","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ji Kaiyi","year":"2021","unstructured":"Kaiyi Ji, Junjie Yang, and Yingbin Liang. 2021. Bilevel optimization: Convergence analysis and enhanced design. In Proceedings of the International Conference on Machine Learning. PMLR, 4882\u20134892."},{"key":"e_1_3_4_118_2","unstructured":"Junguang Jiang Yang Shu Jianmin Wang and Mingsheng Long. 2022. Transferability in deep learning: A survey. arXiv:2201.05867. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2201.05867"},{"key":"e_1_3_4_119_2","doi-asserted-by":"crossref","first-page":"24203","DOI":"10.1109\/CVPR52729.2023.02318","volume-title":"Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Jiang Jingjing","year":"2023","unstructured":"Jingjing Jiang and Nanning Zheng. 2023. MixPHM: Redundancy-aware parameter-efficient tuning for low-resource visual question answering. In Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 24203\u201324213."},{"key":"e_1_3_4_120_2","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-3-031-20044-1_14","volume-title":"Proceedings of the Computer Vision\u2014ECCV 2022: 17th European Conference","author":"Jiang Ziyu","year":"2022","unstructured":"Ziyu Jiang, Tianlong Chen, Xuxi Chen, Yu Cheng, Luowei Zhou, Lu Yuan, Ahmed Awadallah, and Zhangyang Wang. 2022. DnA: Improving few-shot transfer learning with Low-rank decomposition and alignment. In Proceedings of the Computer Vision\u2014ECCV 2022: 17th European Conference. 239\u2013256."},{"issue":"9","key":"e_1_3_4_121_2","doi-asserted-by":"crossref","first-page":"12459","DOI":"10.1109\/TNNLS.2023.3263176","article-title":"Marginalized augmented few-shot domain adaptation","volume":"35","author":"Jing Taotao","year":"2024","unstructured":"Taotao Jing, Haifeng Xia, Jihun Hamm, and Zhengming Ding. 2024. Marginalized augmented few-shot domain adaptation. IEEE Transactions on Neural Networks and Learning Systems 35, 9 (2024), 12459\u201312469.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_4_122_2","unstructured":"Sham M. Kakade and Ambuj Tewari. 2008. On the generalization ability of online strongly convex programming algorithms. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_123_2","unstructured":"Aya Kayal Eduardo Pignatelli and Laura Toni. 2025. The impact of intrinsic rewards on exploration in Reinforcement Learning. Neural Computing and Applications (2025) 1\u201335."},{"key":"e_1_3_4_124_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Khan Zaid","year":"2023","unstructured":"Zaid Khan and Yun Fu. 2023. Contrastive alignment of vision to language through parameter-efficient transfer learning. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_4_125_2","first-page":"30271","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Khanduri Prashant","year":"2021","unstructured":"Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, and Zhuoran Yang. 2021. A near-optimal algorithm for stochastic bilevel optimization via double-momentum. In Proceedings of the Advances in Neural Information Processing Systems. 30271\u201330283."},{"key":"e_1_3_4_126_2","doi-asserted-by":"crossref","unstructured":"Arsham Gholamzadeh Khoee Yinan Yu and Robert Feldt. 2024. Domain generalization through meta-learning: A survey. Artificial Intelligence Review 57 10 (2024) 285.","DOI":"10.1007\/s10462-024-10922-z"},{"key":"e_1_3_4_127_2","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Kim Jongmin","year":"2019","unstructured":"Jongmin Kim, Taesup Kim, Sungwoong Kim, and Chang D. Yoo. 2019. Edge-labeling graph neural network for few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"issue":"12","key":"e_1_3_4_128_2","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.14778\/2367502.2367572","article-title":"Challenges and opportunities with big data","volume":"5","author":"Labrinidis Alexandros","year":"2012","unstructured":"Alexandros Labrinidis and Hosagrahar V. Jagadish. 2012. Challenges and opportunities with big data. Proceedings of the VLDB Endowment 5, 12 (2012), 2032\u20132033.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_3_4_129_2","first-page":"16520","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Lan Lin","year":"2020","unstructured":"Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, and Xiaohong Guan. 2020. Node classification on graphs with few-shot novel labels via meta transformed network embedding. In Proceedings of the Advances in Neural Information Processing Systems. 16520\u201316531."},{"key":"e_1_3_4_130_2","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1145\/3638530.3654238","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","author":"Lange Robert","year":"2024","unstructured":"Robert Lange, Yingtao Tian, and Yujin Tang. 2024. Large language models as evolution strategies. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 579\u2013582."},{"key":"e_1_3_4_131_2","first-page":"273","article-title":"Tutorial on practical prediction theory for classification","volume":"6","author":"Langford John","year":"2005","unstructured":"John Langford. 2005. Tutorial on practical prediction theory for classification. Journal of Machine Learning Research 6, Mar(2005), 273\u2013306.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_4_132_2","first-page":"3672","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Law Marc","year":"2019","unstructured":"Marc Law, Renjie Liao, Jake Snell, and Richard Zemel. 2019. Lorentzian distance learning for hyperbolic representations. In Proceedings of the International Conference on Machine Learning. 3672\u20133681."},{"issue":"7553","key":"e_1_3_4_133_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553, 436\u2013444.","journal-title":"Nature"},{"issue":"1","key":"e_1_3_4_134_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/72.554193","article-title":"Decision boundary feature extraction for neural networks","volume":"8","author":"Lee Chulhee","year":"1997","unstructured":"Chulhee Lee and David A. Landgrebe. 1997. Decision boundary feature extraction for neural networks. IEEE Transactions on Neural Networks 8, 1 (1997), 75\u201383.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"e_1_3_4_135_2","volume-title":"Proceedings of the 42nd International Conference on Machine Learning","author":"Lee Hojoon","year":"2025","unstructured":"Hojoon Lee, Youngdo Lee, Takuma Seno, Donghu Kim, Peter Stone, and Jaegul Choo. 2025. Hyperspherical normalization for scalable deep reinforcement learning. In Proceedings of the 42nd International Conference on Machine Learning."},{"key":"e_1_3_4_136_2","volume-title":"Riemannian Manifolds: An Introduction to Curvature","author":"Lee John M.","year":"2006","unstructured":"John M. Lee. 2006. Riemannian Manifolds: An Introduction to Curvature. Vol. 176. Springer Science and Business Media."},{"key":"e_1_3_4_137_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Lee Su Young","year":"2019","unstructured":"Su Young Lee, Choi Sungik, and Sae-Young Chung. 2019. Sample-efficient deep reinforcement learning via episodic backward update. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_138_2","first-page":"8152","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Lei Tao","year":"2023","unstructured":"Tao Lei, Junwen Bai, Siddhartha Brahma, Joshua Ainslie, Kenton Lee, Yanqi Zhou, Nan Du, Vincent Zhao, Yuexin Wu, Bo Li, et\u00a0al. 2023. Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference. In Proceedings of the Advances in Neural Information Processing Systems. 8152\u20138172."},{"key":"e_1_3_4_139_2","doi-asserted-by":"crossref","unstructured":"Brian Lester Rami Al-Rfou and Noah Constant. 2021. The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 3045\u20133059.","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"e_1_3_4_140_2","doi-asserted-by":"crossref","unstructured":"Da Li Yongxin Yang Yi-Zhe Song and Timothy Hospedales. 2018. Learning to generalize: Meta-learning for domain generalization. In Proceedings of the AAAI Conference on Artificial Intelligence 32 1 (2018).","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"e_1_3_4_141_2","first-page":"50509","volume-title":"Proceedings of the International Conference on Representation Learning","author":"Li Hengjia","year":"2024","unstructured":"Hengjia Li, Yang Liu, Linxuan Xia, Yuqi Lin, Wenxiao Wang, Tu Zheng, Zheng Yang, Xiaohui Zhong, Xiaobo Ren, and Xiaofei He. 2024. Few-shot hybrid domain adaptation of image generator. In Proceedings of the International Conference on Representation Learning. 50509\u201350528."},{"key":"e_1_3_4_142_2","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/CVPR46437.2021.00029","volume-title":"Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Li Lei","year":"2021","unstructured":"Lei Li, Ke Gao, Juan Cao, Ziyao Huang, Yepeng Weng, Xiaoyue Mi, Zhengze Yu, Xiaoya Li, and Boyang Xia. 2021. Progressive domain expansion network for single domain generalization. In Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 224\u2013233."},{"key":"e_1_3_4_143_2","volume-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","author":"Li Long-Fei","year":"2024","unstructured":"Long-Fei Li, Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou. 2024. Provably efficient reinforcement learning with multinomial logit function approximation. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_4_144_2","first-page":"9574","volume-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Li Qimai","year":"2019","unstructured":"Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, and Zhichao Guan. 2019. Label efficient semi-supervised learning via graph filtering. In Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9574\u20139583."},{"issue":"9","key":"e_1_3_4_145_2","doi-asserted-by":"crossref","first-page":"4260","DOI":"10.1109\/TIP.2018.2839528","article-title":"Domain invariant and class discriminative feature learning for visual domain adaptation","volume":"27","author":"Li Shuang","year":"2018","unstructured":"Shuang Li, Shiji Song, Gao Huang, Zhengming Ding, and Cheng Wu. 2018. Domain invariant and class discriminative feature learning for visual domain adaptation. IEEE Transactions on Image Processing 27, 9 (2018), 4260\u20134273.","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_3_4_146_2","first-page":"20035","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Li Wenhao","year":"2023","unstructured":"Wenhao Li, Xiangfeng Wang, Bo Jin, and Hongyuan Zha. 2023. Hierarchical diffusion for offline decision making. In Proceedings of the 40th International Conference on Machine Learning. 20035\u201320064."},{"key":"e_1_3_4_147_2","first-page":"7151","volume-title":"Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Li Wei-Hong","year":"2022","unstructured":"Wei-Hong Li, Xialei Liu, and Hakan Bilen. 2022. Cross-domain Few-shot Learning with Task-specific Adapters. In Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7151\u20137160."},{"issue":"1","key":"e_1_3_4_148_2","first-page":"334","article-title":"Towards safe weakly supervised learning","volume":"43","author":"Li Yu-Feng","year":"2019","unstructured":"Yu-Feng Li, Lan-Zhe Guo, and Zhi-Hua Zhou. 2019. Towards safe weakly supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 1 (2019), 334\u2013346.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_4_149_2","first-page":"29128","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Li Ziniu","year":"2024","unstructured":"Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, and Zhi-Quan Luo. 2024. ReMax: A simple, effective, and efficient reinforcement learning method for aligning large language models. In Proceedings of the 41st International Conference on Machine Learning. PMLR, 29128\u201329163."},{"key":"e_1_3_4_150_2","first-page":"9404","volume-title":"Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Liang Hanwen","year":"2021","unstructured":"Hanwen Liang, Qiong Zhang, Peng Dai, and Juwei Lu. 2021. Boosting the generalization capability in cross-domain few-shot learning via noise-enhanced supervised autoencoder. In Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). 9404\u20139414."},{"issue":"10","key":"e_1_3_4_151_2","doi-asserted-by":"crossref","first-page":"14903","DOI":"10.1109\/TNNLS.2023.3282049","article-title":"Comprehensive graph gradual pruning for sparse training in graph neural networks","volume":"35","author":"Liu Chuang","year":"2024","unstructured":"Chuang Liu, Xueqi Ma, Yibing Zhan, Liang Ding, Dapeng Tao, Bo Du, Wenbin Hu, and Danilo P. Mandic. 2024. Comprehensive graph gradual pruning for sparse training in graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 35, 10 (2024), 14903\u201314917.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_4_152_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Liu Chang","year":"2018","unstructured":"Chang Liu and Jun Zhu. 2018. Riemannian stein variational gradient descent for bayesian inference. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_4_153_2","first-page":"178","volume-title":"Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization","author":"Liu Fei","year":"2025","unstructured":"Fei Liu, Xi Lin, Shunyu Yao, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, and Qingfu Zhang. 2025. Large language model for multiobjective evolutionary optimization. In Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization. Springer, 178\u2013191."},{"key":"e_1_3_4_154_2","volume-title":"Proceedings of the Forty-first International Conference on Machine Learning","author":"Liu Fei","year":"2024","unstructured":"Fei Liu, Tong Xialiang, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang. 2024. Evolution of heuristics: Towards efficient automatic algorithm design using large language model. In Proceedings of the Forty-first International Conference on Machine Learning."},{"key":"e_1_3_4_155_2","volume-title":"Proceedings of the IJCAI International Joint Conference on Artificial Intelligence","author":"Liu L.","year":"2019","unstructured":"L. Liu, T. Zhou, G. Long, J. Jiang, L. Yao, and C. Zhang. 2019. Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph. In Proceedings of the IJCAI International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_4_156_2","first-page":"276","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Liu Qiang","year":"2016","unstructured":"Qiang Liu, Jason Lee, and Michael Jordan. 2016. A kernelized Stein discrepancy for goodness-of-fit tests. In Proceedings of the International Conference on Machine Learning. PMLR, 276\u2013284."},{"key":"e_1_3_4_157_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Liu Qi","year":"2019","unstructured":"Qi Liu, Maximilian Nickel, and Douwe Kiela. 2019. Hyperbolic graph neural networks. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_158_2","unstructured":"Qiang Liu and Dilin Wang. 2016. Stein variational gradient descent: A general purpose bayesian inference algorithm. In Advances in Neural Information Processing Systems. Curran Associates Inc."},{"key":"e_1_3_4_159_2","first-page":"12687","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Liu Shihong","year":"2024","unstructured":"Shihong Liu, Samuel Yu, Zhiqiu Lin, Deepak Pathak, and Deva Ramanan. 2024. Language models as black-box optimizers for vision-language models. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 12687\u201312697."},{"key":"e_1_3_4_160_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Liu Yanbin","year":"2019","unstructured":"Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sungju Hwang, and Yi Yang. 2019. Learning to propagate labels: Transductive propagation network for few-shot learning. In Proceedings of the International Conference on Learning Representations."},{"issue":"5","key":"e_1_3_4_161_2","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1609\/aaai.v35i5.16551","article-title":"Relative and absolute location embedding for few-shot node classification on graph","volume":"35","author":"Liu Zemin","year":"2021","unstructured":"Zemin Liu, Yuan Fang, Chenghao Liu, and Steven C. H. Hoi. 2021. Relative and absolute location embedding for few-shot node classification on graph. Proceedings of the AAAI Conference on Artificial Intelligence 35, 5 (2021), 4267\u20134275.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_4_162_2","first-page":"86602","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Liu Zhishuai","year":"2024","unstructured":"Zhishuai Liu and Pan Xu. 2024. Minimax optimal and computationally efficient algorithms for distributionally robust offline reinforcement learning. In Proceedings of the Advances in Neural Information Processing Systems. 86602\u201386654."},{"key":"e_1_3_4_163_2","first-page":"22594","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Lobel Sam","year":"2023","unstructured":"Sam Lobel, Akhil Bagaria, and George Konidaris. 2023. Flipping coins to estimate pseudocounts for exploration in reinforcement learning. In Proceedings of the 40th International Conference on Machine Learning. 22594\u201322613."},{"key":"e_1_3_4_164_2","doi-asserted-by":"crossref","unstructured":"Shayne Longpre Robert Mahari Ariel Lee Campbell Lund Hamidah Oderinwale William Brannon Nayan Saxena Naana Obeng-Marnu Tobin South Cole Hunter Kevin Klyman Klamm and others. 2024. Consent in crisis: The rapid decline of the AI data commons. In Advances in Neural Information Processing Systems. 108042\u2013108087.","DOI":"10.52202\/079017-3431"},{"key":"e_1_3_4_165_2","first-page":"6393","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Lou Aaron","year":"2020","unstructured":"Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, and Christopher De Sa. 2020. Differentiating through the fr\u00e9chet mean. In Proceedings of the International Conference on Machine Learning. PMLR, 6393\u20136403."},{"key":"e_1_3_4_166_2","first-page":"24577","volume-title":"Proceedings of the International Conference on Representation Learning","author":"Lu Haoyu","year":"2024","unstructured":"Haoyu Lu, Yuqi Huo, Guoxing Yang, Zhiwu Lu, Wei Zhan, Masayoshi Tomizuka, and Mingyu Ding. 2024. UniAdapter: Unified parameter-efficient transfer learning for cross-modal modeling. In Proceedings of the International Conference on Representation Learning. 24577\u201324596."},{"key":"e_1_3_4_167_2","first-page":"526","volume-title":"Proceedings of the 1st Conference on Causal Learning and Reasoning","author":"Lu Yangyi","year":"2022","unstructured":"Yangyi Lu, Amirhossein Meisami, and Ambuj Tewari. 2022. Efficient reinforcement learning with prior causal knowledge. In Proceedings of the 1st Conference on Causal Learning and Reasoning. 526\u2013541."},{"key":"e_1_3_4_168_2","first-page":"4183","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Lu\u010di\u0107 Mario","year":"2019","unstructured":"Mario Lu\u010di\u0107, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, and Sylvain Gelly. 2019. High-fidelity image generation with fewer labels. In Proceedings of the International Conference on Machine Learning. PMLR, 4183\u20134192."},{"key":"e_1_3_4_169_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Luo Yawei","year":"2020","unstructured":"Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, and Yi Yang. 2020. Adversarial style mining for one-shot unsupervised domain adaptation. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_170_2","volume-title":"Proceedings of the 42nd International Conference on Machine Learning","author":"Ma Haitong","year":"2025","unstructured":"Haitong Ma, Tianyi Chen, Kai Wang, Na Li, and Bo Dai. 2025. Efficient online reinforcement learning for diffusion policy. In Proceedings of the 42nd International Conference on Machine Learning."},{"key":"e_1_3_4_171_2","first-page":"33730","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Ma Yi","year":"2024","unstructured":"Yi Ma, Jianye Hao, Hebin Liang, and Chenjun Xiao. 2024. Rethinking decision transformer via hierarchical reinforcement learning. In Proceedings of the 41st International Conference on Machine Learning. 33730\u201333745."},{"key":"e_1_3_4_172_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Mai Vincent","year":"2022","unstructured":"Vincent Mai, Kaustubh Mani, and Liam Paull. 2022. Sample efficient deep reinforcement learning via uncertainty estimation. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_173_2","first-page":"7780","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Mitchell Eric","year":"2021","unstructured":"Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, and Chelsea Finn. 2021. Offline meta-reinforcement learning with advantage weighting. In Proceedings of the International Conference on Machine Learning. PMLR, 7780\u20137791."},{"key":"e_1_3_4_174_2","first-page":"305","volume-title":"Proceedings of the 5th International Joint Conference on Artificial Intelligence-volume 1","author":"Mitchell Tom M.","year":"1977","unstructured":"Tom M. Mitchell. 1977. Version spaces: A candidate elimination approach to rule learning. In Proceedings of the 5th International Joint Conference on Artificial Intelligence-volume 1. 305\u2013310."},{"issue":"7540","key":"e_1_3_4_175_2","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih Volodymyr","year":"2015","unstructured":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et\u00a0al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529\u2013533.","journal-title":"Nature"},{"issue":"1","key":"e_1_3_4_176_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1022635613229","article-title":"Prioritized sweeping: Reinforcement learning with less data and less time","volume":"13","author":"Moore Andrew W.","year":"1993","unstructured":"Andrew W. Moore and Christopher G. Atkeson. 1993. Prioritized sweeping: Reinforcement learning with less data and less time. Machine learning 13, 1(1993), 103\u2013130.","journal-title":"Machine learning"},{"key":"e_1_3_4_177_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Motiian Saeid","year":"2017","unstructured":"Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto. 2017. Few-shot adversarial domain adaptation. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_178_2","unstructured":"Krikamol Muandet Bharath Sriperumbudur Kenji Fukumizu Arthur Gretton and Bernhard Sch\u00f6lkopf. 2016. Kernel mean shrinkage estimators. Journal of Machine Learning Research 17 48 (2016) 1\u201341."},{"key":"e_1_3_4_179_2","first-page":"3307","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Nachum Ofir","year":"2018","unstructured":"Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, and Sergey Levine. 2018. Data-efficient hierarchical reinforcement learning. In Proceedings of the Advances in Neural Information Processing Systems. 3307\u20133317."},{"issue":"12","key":"e_1_3_4_180_2","first-page":"1510","article-title":"Kimera: From SLAM to spatial perception with 3D dynamic scene graphs","volume":"40","author":"Nanayakkara Thrishantha","year":"2021","unstructured":"Thrishantha Nanayakkara, Tim Barfoot, Thomas Howard, Antoni Rosinol, Andrew Violette, Marcus Abate, Nathan Hughes, Yun Chang, Jingnan Shi, Arjun Gupta, et\u00a0al. 2021. Kimera: From SLAM to spatial perception with 3D dynamic scene graphs. International Journal of Robotics Research 40, 12\u201314 (2021), 1510\u20131546.","journal-title":"International Journal of Robotics Research"},{"key":"e_1_3_4_181_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Nickel Maximillian","year":"2017","unstructured":"Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_182_2","volume-title":"Proceedings of the NeurIPS 2023 Foundation Models for Decision Making Workshop","author":"Nie Allen","unstructured":"Allen Nie, Ching-An Cheng, Andrey Kolobov, and Adith Swaminathan. [n.d.]. Importance of directional feedback for LLM-based optimizers. In Proceedings of the NeurIPS 2023 Foundation Models for Decision Making Workshop."},{"key":"e_1_3_4_183_2","volume-title":"Principles of Artificial Intelligence","author":"Nilsson Nils J.","year":"2014","unstructured":"Nils J. Nilsson. 2014. Principles of Artificial Intelligence. Morgan Kaufmann."},{"key":"e_1_3_4_184_2","first-page":"26311","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Nottingham Kolby","year":"2023","unstructured":"Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, and Roy Fox. 2023. Do embodied agents dream of pixelated sheep: Embodied decision making using language guided world modelling. In Proceedings of the International Conference on Machine Learning. PMLR, 26311\u201326325."},{"key":"e_1_3_4_185_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Oh Jaehoon","year":"2022","unstructured":"Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, and Se-Young Yun. 2022. Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_186_2","doi-asserted-by":"crossref","first-page":"10738","DOI":"10.1109\/CVPR46437.2021.01060","volume-title":"Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Ojha Utkarsh","year":"2021","unstructured":"Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, and Richard Zhang. 2021. Few-shot image generation via cross-domain correspondence. In Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10738\u201310747."},{"issue":"2","key":"e_1_3_4_187_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MIS.2013.39","article-title":"Artificial intelligence and big data","volume":"28","author":"O\u2019Leary Daniel E.","year":"2013","unstructured":"Daniel E. O\u2019Leary. 2013. Artificial intelligence and big data. IEEE Intelligent Systems 28, 2 (2013), 96\u201399.","journal-title":"IEEE Intelligent Systems"},{"key":"e_1_3_4_188_2","first-page":"2721","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ostrovski Georg","year":"2017","unstructured":"Georg Ostrovski, Marc G. Bellemare, A\u00e4ron Oord, and R\u00e9mi Munos. 2017. Count-based exploration with neural density models. In Proceedings of the International Conference on Machine Learning. PMLR, 2721\u20132730."},{"key":"e_1_3_4_189_2","first-page":"26462","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"35","author":"Pan Junting","year":"2022","unstructured":"Junting Pan, Ziyi Lin, Xiatian Zhu, Jing Shao, and Hongsheng Li. 2022. ST-Adapter: Parameter-efficient image-to-video transfer learning. In Proceedings of the Advances in Neural Information Processing Systems. S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35, 26462\u201326477."},{"key":"e_1_3_4_190_2","first-page":"488","volume-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops","author":"Pathak Deepak","year":"2017","unstructured":"Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. 2017. Curiosity-driven exploration by self-supervised prediction. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. 488\u2013489."},{"issue":"12","key":"e_1_3_4_191_2","doi-asserted-by":"crossref","first-page":"10023","DOI":"10.1109\/TPAMI.2021.3136921","article-title":"Hyperbolic deep neural networks: A survey","volume":"44","author":"Peng Wei","year":"2021","unstructured":"Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, and Guoying Zhao. 2021. Hyperbolic deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 12 (2021), 10023\u201310044.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_4_192_2","first-page":"27720","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Phung Trung","year":"2021","unstructured":"Trung Phung, Trung Le, Tung-Long Vuong, Toan Tran, Anh Tran, Hung Bui, and Dinh Phung. 2021. On learning domain-invariant representations for transfer learning with multiple sources. In Proceedings of the Advances in Neural Information Processing Systems. 27720\u201327733."},{"key":"e_1_3_4_193_2","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1145\/3583133.3596401","volume-title":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","author":"Pluhacek Michal","year":"2023","unstructured":"Michal Pluhacek, Anezka Kazikova, Tomas Kadavy, Adam Viktorin, and Roman Senkerik. 2023. Leveraging large language models for the generation of novel metaheuristic optimization algorithms. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation. 1812\u20131820."},{"key":"e_1_3_4_194_2","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","author":"Pryzant Reid","year":"2023","unstructured":"Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng. 2023. Automatic prompt optimization with \u201dGradient Descent\u201d and beam search. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing."},{"key":"e_1_3_4_195_2","doi-asserted-by":"crossref","first-page":"12553","DOI":"10.1109\/CVPR42600.2020.01257","volume-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Qiao Fengchun","year":"2020","unstructured":"Fengchun Qiao, Long Zhao, and Xi Peng. 2020. Learning to learn single domain generalization. In Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12553\u201312562."},{"key":"e_1_3_4_196_2","first-page":"15397","volume-title":"Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Qiao Yanyuan","year":"2023","unstructured":"Yanyuan Qiao, Zheng Yu, and Qi Wu. 2023. VLN-PETL: Parameter-efficient transfer learning for vision-and-language navigation. In Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV). 15397\u201315406."},{"key":"e_1_3_4_197_2","first-page":"15900","volume-title":"Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Qin Xiaorong","year":"2023","unstructured":"Xiaorong Qin, Xinhang Song, and Shuqiang Jiang. 2023. Bi-level meta-learning for few-shot domain generalization. In Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 15900\u201315910."},{"key":"e_1_3_4_198_2","first-page":"7867","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Qu Meng","year":"2020","unstructured":"Meng Qu, Tianyu Gao, Louis-Pascal Xhonneux, and Jian Tang. 2020. Few-shot relation extraction via Bayesian Meta-learning on relation graphs. In Proceedings of the 37th International Conference on Machine Learning. 7867\u20137876."},{"key":"e_1_3_4_199_2","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. (2018). Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/openai.com\/index\/language-unsupervised\/"},{"key":"e_1_3_4_200_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Raghu Maithra","year":"2019","unstructured":"Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio. 2019. Transfusion: Understanding transfer learning for medical imaging. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_201_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Raghuram Jayaram","year":"2023","unstructured":"Jayaram Raghuram, Yijing Zeng, Dolores Garcia, Rafael Ruiz, Somesh Jha, Joerg Widmer, and Suman Banerjee. 2023. Few-shot domain adaptation for end-to-end communication. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_4_202_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Raileanu Roberta","year":"2020","unstructured":"Roberta Raileanu and Tim Rockt\u00e4schel. 2020. RIDE: Rewarding impact-driven exploration for procedurally-generated environments. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_203_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Rajeswaran Aravind","year":"2019","unstructured":"Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, and Sergey Levine. 2019. Meta-learning with implicit gradients. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_204_2","first-page":"5331","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Rakelly Kate","year":"2019","unstructured":"Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen. 2019. Efficient off-policy meta-reinforcement learning via probabilistic context variables. In Proceedings of the 36th International Conference on Machine Learning. 5331\u20135340."},{"issue":"9","key":"e_1_3_4_205_2","first-page":"1","article-title":"A survey of deep active learning","volume":"54","author":"Ren Pengzhen","year":"2021","unstructured":"Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, and Xin Wang. 2021. A survey of deep active learning. ACM Computing Surveys 54, 9 (2021), 1\u201340.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_4_206_2","first-page":"1530","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Rezende Danilo","year":"2015","unstructured":"Danilo Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. In Proceedings of the International Conference on Machine Learning. PMLR, 1530\u20131538."},{"key":"e_1_3_4_207_2","volume-title":"A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems","author":"Rodrigues Francisco Aparecido","year":"2019","unstructured":"Francisco Aparecido Rodrigues. 2019. Network centrality: An introduction. A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems. Springer."},{"key":"e_1_3_4_208_2","first-page":"2152","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Romera-Paredes Bernardino","year":"2015","unstructured":"Bernardino Romera-Paredes and Philip Torr. 2015. An embarrassingly simple approach to zero-shot learning. In Proceedings of the International Conference on Machine Learning. PMLR, 2152\u20132161."},{"key":"e_1_3_4_209_2","unstructured":"Nicholas Roy and Andrew McCallum. 2001. Toward Optimal Active Learning through Sampling Estimation of Error Reduction. In Proceedings of the Eighteenth International Conference on Machine Learning. San Francisco CA USA 441\u2013448."},{"key":"e_1_3_4_210_2","unstructured":"Sebastian Ruder. 2017. An overview of gradient descent optimization algorithms. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/1609.04747"},{"key":"e_1_3_4_211_2","unstructured":"Stuart Russell and Peter Norvig. 2002. Artificial intelligence: A modern approach (2rd ed.). Prentice Hall Press USA."},{"key":"e_1_3_4_212_2","first-page":"9190","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Rybkin Oleh","year":"2021","unstructured":"Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, and Sergey Levine. 2021. Model-based reinforcement learning via latent-space collocation. In Proceedings of the 38th International Conference on Machine Learning. 9190\u20139201."},{"key":"e_1_3_4_213_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Satorras Victor Garcia","year":"2018","unstructured":"Victor Garcia Satorras and Joan Bruna Estrach. 2018. Few-shot learning with graph neural networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_214_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Savinov Nikolay","year":"2019","unstructured":"Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphael Marinier, Marc Pollefeys, Timothy Lillicrap, and Sylvain Gelly. 2019. Episodic curiosity through reachability. In Proceedings of the International Conference on Learning Representations."},{"issue":"7698","key":"e_1_3_4_215_2","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1038\/nature25978","article-title":"Planning chemical syntheses with deep neural networks and symbolic AI","volume":"555","author":"Segler Marwin H. S.","year":"2018","unstructured":"Marwin H. S. Segler, Mike Preuss, and Mark P. Waller. 2018. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 7698 (2018), 604\u2013610.","journal-title":"Nature"},{"key":"e_1_3_4_216_2","unstructured":"Burr Settles. 2009. Active learning literature survey. (2009). Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/minds.wisconsin.edu\/handle\/1793\/60660"},{"issue":"1","key":"e_1_3_4_217_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-01560-1","article-title":"Active learning","volume":"6","author":"Settles Burr","year":"2012","unstructured":"Burr Settles. 2012. Active learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 6, 1 (2012), 1\u2013114.","journal-title":"Synthesis Lectures on Artificial Intelligence and Machine Learning"},{"issue":"12","key":"e_1_3_4_218_2","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1109\/TPAMI.2018.2789887","article-title":"Unsupervised deep hashing with similarity-adaptive and discrete optimization","volume":"40","author":"Shen Fumin","year":"2018","unstructured":"Fumin Shen, Yan Xu, Li Liu, Yang Yang, Zi Huang, and Heng Tao Shen. 2018. Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 12 (2018), 3034\u20133044.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"218","key":"e_1_3_4_219_2","first-page":"1","article-title":"Adaptation augmented model-based policy optimization","volume":"24","author":"Shen Jian","year":"2023","unstructured":"Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, and Weinan Zhang. 2023. Adaptation augmented model-based policy optimization. Journal of Machine Learning Research 24, 218 (2023), 1\u201335.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_4_220_2","unstructured":"Yong-Min Shin and Won-Yong Shin. 2024. On the feasibility of fidelity\u2013 for graph pruning. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2406.11504"},{"key":"e_1_3_4_221_2","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Song Xingyou","year":"2024","unstructured":"Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang, and Yutian Chen. 2024. Position: Leverage foundational models for black-box optimization. In Proceedings of the 41st International Conference on Machine Learning."},{"issue":"13","key":"e_1_3_4_222_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3582688","article-title":"A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities","volume":"55","author":"Song Yisheng","year":"2023","unstructured":"Yisheng Song, Ting Wang, Puyu Cai, Subrota K. Mondal, and Jyoti Prakash Sahoo. 2023. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. Comput. Surveys 55, 13s (2023), 1\u201340.","journal-title":"Comput. Surveys"},{"key":"e_1_3_4_223_2","first-page":"403","volume-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Sun Qianru","year":"2019","unstructured":"Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele. 2019. Meta-transfer learning for few-shot learning. In Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 403\u2013412."},{"key":"e_1_3_4_224_2","first-page":"1199","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Sung Flood","year":"2018","unstructured":"Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales. 2018. Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1199\u20131208."},{"key":"e_1_3_4_225_2","first-page":"12991","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Sung Yi-Lin","year":"2022","unstructured":"Yi-Lin Sung, Jaemin Cho, and Mohit Bansal. 2022. LST: Ladder side-tuning for parameter and memory efficient transfer learning. In Proceedings of the Advances in Neural Information Processing Systems. Curran Associates, Inc., 12991\u201313005."},{"key":"e_1_3_4_226_2","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1109\/CVPR52688.2022.00516","volume-title":"Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Sung Yi-Lin","year":"2022","unstructured":"Yi-Lin Sung, Jaemin Cho, and Mohit Bansal. 2022. VL-ADAPTER: Parameter-efficient transfer learning for vision-and-language tasks. In Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5217\u20135227."},{"issue":"4","key":"e_1_3_4_227_2","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1145\/122344.122377","article-title":"Dyna, an integrated architecture for learning, planning, and reacting","volume":"2","author":"Sutton Richard S.","year":"1991","unstructured":"Richard S. Sutton. 1991. Dyna, an integrated architecture for learning, planning, and reacting. ACM SIGART Bulletin 2, 4 (1991), 160\u2013163.","journal-title":"ACM SIGART Bulletin"},{"key":"e_1_3_4_228_2","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1145\/3626772.3657775","volume-title":"Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Tang Jiabin","year":"2024","unstructured":"Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. 2024. GraphGPT: Graph instruction tuning for large language models. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 491\u2013500."},{"key":"e_1_3_4_229_2","first-page":"25264","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Tang Xinyu","year":"2025","unstructured":"Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, Siyuan Lu, Yaliang Li, and Ji-Rong Wen. 2025. Unleashing the potential of large language models as prompt optimizers: Analogical analysis with gradient-based model optimizers. In Proceedings of the AAAI Conference on Artificial Intelligence. 25264\u201325272."},{"key":"e_1_3_4_230_2","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Teshima Takeshi","year":"2020","unstructured":"Takeshi Teshima, Issei Sato, and Masashi Sugiyama. 2020. Few-shot domain adaptation by causal mechanism transfer. In Proceedings of the 37th International Conference on Machine Learning."},{"key":"e_1_3_4_231_2","first-page":"2718","volume-title":"Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence","author":"Tian Pinzhuo","year":"2021","unstructured":"Pinzhuo Tian, Lei Qi, Shaokang Dong, Yinghuan Shi, and Yang Gao. 2021. Consistent MetaReg: Alleviating intra-task discrepancy for better meta-knowledge. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. 2718\u20132724."},{"issue":"221","key":"e_1_3_4_232_2","first-page":"1","article-title":"Behavior priors for efficient reinforcement learning","volume":"23","author":"Tirumala Dhruva","year":"2022","unstructured":"Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, et\u00a0al. 2022. Behavior priors for efficient reinforcement learning. Journal of Machine Learning Research 23, 221 (2022), 1\u201368.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_4_233_2","volume-title":"Proceedings of the International Conference on Representation Learning","author":"Quan Ioannis Antonoglou David Silver Tom Schaul, John","year":"2016","unstructured":"Ioannis Antonoglou David Silver Tom Schaul, John Quan. 2016. Prioritized experience replay. In Proceedings of the International Conference on Representation Learning."},{"key":"e_1_3_4_234_2","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar and others. 2023. LLaMA: Open and efficient foundation language models. Retrieved from https:\/\/linproxy.fan.workers.dev:443\/https\/arxiv.org\/abs\/2302.13971"},{"issue":"11","key":"e_1_3_4_235_2","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1145\/1968.1972","article-title":"A theory of the learnable","volume":"27","author":"Valiant Leslie G.","year":"1984","unstructured":"Leslie G. Valiant. 1984. A theory of the learnable. Commun. ACM 27, 11 (1984), 1134\u20131142.","journal-title":"Commun. ACM"},{"key":"e_1_3_4_236_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_237_2","unstructured":"Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu and Daan Wierstra. 2016. Matching networks for one shot learning. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_4_238_2","first-page":"5339","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Volpi Riccardo","year":"2018","unstructured":"Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to unseen domains via adversarial data augmentation. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 5339\u20135349."},{"key":"e_1_3_4_239_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Vuorio Risto","year":"2019","unstructured":"Risto Vuorio, Shao-Hua Sun, Hexiang Hu, and Joseph J. Lim. 2019. Multimodal model-agnostic meta-learning via task-aware modulation. In Proceedings of the Advances in Neural Information Processing Systems."},{"issue":"2","key":"e_1_3_4_240_2","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/JAS.2016.7471613","article-title":"Where does alphago go: From church-turing thesis to AlphaGo thesis and beyond","volume":"3","author":"Wang Fei-Yue","year":"2016","unstructured":"Fei-Yue Wang, Jun Jason Zhang, Xinhu Zheng, Xiao Wang, Yong Yuan, Xiaoxiao Dai, Jie Zhang, and Liuqing Yang. 2016. Where does alphago go: From church-turing thesis to AlphaGo thesis and beyond. IEEE\/CAA Journal of Automatica Sinica 3, 2 (2016), 113\u2013120.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"e_1_3_4_241_2","first-page":"9837","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wang Lingxiao","year":"2020","unstructured":"Lingxiao Wang, Qi Cai, Zhuoran Yang, and Zhaoran Wang. 2020. On the global optimality of model-agnostic meta-learning. In Proceedings of the International Conference on Machine Learning. PMLR, 9837\u20139846."},{"key":"e_1_3_4_242_2","first-page":"38925","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Wang Song","year":"2022","unstructured":"Song Wang, Chen Chen, and Jundong Li. 2022. Graph few-shot learning with task-specific structures. In Proceedings of the Advances in Neural Information Processing Systems. 38925\u201338936."},{"key":"e_1_3_4_243_2","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1145\/3534678.3539265","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Wang Song","year":"2022","unstructured":"Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, and Jundong Li. 2022. Task-adaptive few-shot node classification. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1910\u20131919."},{"issue":"12","key":"e_1_3_4_244_2","doi-asserted-by":"crossref","first-page":"15018","DOI":"10.1109\/TPAMI.2023.3306352","article-title":"MMT: Cross domain few-shot learning via meta-memory transfer","volume":"45","author":"Wang Wenjian","year":"2023","unstructured":"Wenjian Wang, Lijuan Duan, Yuxi Wang, Junsong Fan, and Zhaoxiang Zhang. 2023. MMT: Cross domain few-shot learning via meta-memory transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 12 (2023), 15018\u201315035.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"e_1_3_4_245_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3386252","article-title":"Generalizing from a few examples: A survey on few-shot learning","volume":"53","author":"Wang Yaqing","year":"2020","unstructured":"Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2020. Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys 53, 3 (2020), 1\u201334.","journal-title":"ACM Computing Surveys"},{"issue":"3","key":"e_1_3_4_246_2","doi-asserted-by":"crossref","first-page":"5509","DOI":"10.1109\/TNNLS.2024.3381347","article-title":"Geometric matching for cross-modal retrieval","volume":"36","author":"Wang Zheng","year":"2025","unstructured":"Zheng Wang, Zhenwei Gao, Yang Yang, Guoqing Wang, Chengbo Jiao, and Heng Tao Shen. 2025. Geometric matching for cross-modal retrieval. IEEE Transactions on Neural Networks and Learning Systems 36, 3 (2025), 5509\u20135521.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_4_247_2","first-page":"6861","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In Proceedings of the 36th International Conference on Machine Learning. 6861\u20136871."},{"key":"e_1_3_4_248_2","first-page":"37571","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Wu Lirong","year":"2023","unstructured":"Lirong Wu, Haitao Lin, Yufei Huang, and Stan Z. Li. 2023. Quantifying the knowledge in GNNs for reliable distillation into MLPs. In Proceedings of the 40th International Conference on Machine Learning. PMLR, 37571\u201337581."},{"key":"e_1_3_4_249_2","first-page":"57099","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Wu Yi","year":"2023","unstructured":"Yi Wu, Ziqiang Li, Chaoyue Wang, Heliang Zheng, Shanshan Zhao, Bin Li, and Dacheng Tao. 2023. Domain Re-modulation for few-shot generative domain adaptation. In Proceedings of the Advances in Neural Information Processing Systems. 57099\u201357124."},{"key":"e_1_3_4_250_2","first-page":"24332","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Xia Jun","year":"2022","unstructured":"Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, and Stan Z. Li. 2022. Progcl: Rethinking hard negative mining in graph contrastive learning. In Proceedings of the International Conference on Machine Learning. PMLR, 24332\u201324346."},{"key":"e_1_3_4_251_2","volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence","author":"Xia Rui","year":"2013","unstructured":"Rui Xia, Xuelei Hu, Jianfeng Lu, Jian Yang, and Chengqing Zong. 2013. Instance selection and instance weighting for cross-domain sentiment classification via PU learning. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_4_252_2","first-page":"10275","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Xian Yongqin","year":"2019","unstructured":"Yongqin Xian, Saurabh Sharma, Bernt Schiele, and Zeynep Akata. 2019. f-vaegan-d2: A feature generating framework for any-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 10275\u201310284."},{"key":"e_1_3_4_253_2","doi-asserted-by":"crossref","first-page":"11194","DOI":"10.1109\/CVPR52688.2022.01092","volume-title":"Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Xiao Jiayu","year":"2022","unstructured":"Jiayu Xiao, Liang Li, Chaofei Wang, Zheng-Jun Zha, and Qingming Huang. 2022. Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment. In Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11194\u201311203."},{"key":"e_1_3_4_254_2","first-page":"80522","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Xin Yi","year":"2024","unstructured":"Yi Xin, Siqi Luo, Xuyang Liu, Yuntao Du, Haodi Zhou, Xinyu Cheng, Christina Lee, Junlong Du, Haozhe Wang, Mingcai Chen, et\u00a0al. 2024. V-PETL bench: A unified visual parameter-efficient transfer learning benchmark. In Proceedings of the Advances in Neural Information Processing Systems. 80522\u201380535."},{"key":"e_1_3_4_255_2","first-page":"13706","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Xu Xun","year":"2020","unstructured":"Xun Xu and Gim Hee Lee. 2020. Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13706\u201313715."},{"key":"e_1_3_4_256_2","first-page":"1848","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Yan Bencheng","year":"2020","unstructured":"Bencheng Yan, Chaokun Wang, Gaoyang Guo, and Yunkai Lou. 2020. TinyGNN: Learning efficient graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1848\u20131856."},{"key":"e_1_3_4_257_2","volume-title":"Proceedings of the 13th International Conference on Learning Representations","author":"Yan Xue","year":"2025","unstructured":"Xue Yan, Yan Song, Xidong Feng, Mengyue Yang, Haifeng Zhang, Haitham Bou Ammar, and Jun Wang. 2025. Efficient reinforcement learning with large language model priors. In Proceedings of the 13th International Conference on Learning Representations."},{"key":"e_1_3_4_258_2","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"Yang Chengrun","year":"2024","unstructured":"Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen. 2024. Large Language Models as Optimizers. In Proceedings of the 12th International Conference on Learning Representations."},{"key":"e_1_3_4_259_2","first-page":"29761","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Yang Chenxiao","year":"2022","unstructured":"Chenxiao Yang, Qitian Wu, and Junchi Yan. 2022. Geometric knowledge distillation: Topology compression for graph neural networks. In Proceedings of the Advances in Neural Information Processing Systems. 29761\u201329775."},{"key":"e_1_3_4_260_2","first-page":"13670","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Yang Junjie","year":"2021","unstructured":"Junjie Yang, Kaiyi Ji, and Yingbin Liang. 2021. Provably faster algorithms for bilevel optimization. In Proceedings of the Advances in Neural Information Processing Systems. 13670\u201313682."},{"key":"e_1_3_4_261_2","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Yang Ling","year":"2020","unstructured":"Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, and Yu Liu. 2020. DPGN: Distribution propagation graph network for few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_4_262_2","volume-title":"Proceedings of the 37th Conference on Neural Information Processing Systems","author":"Yang Liang","year":"2023","unstructured":"Liang Yang, Runjie Shi, Qiuliang Zhang, Bingxin Niu, Zhen Wang, Xiaochun Cao, and Chuan Wang. 2023. Self-supervised graph neural networks via low-rank decomposition. In Proceedings of the 37th Conference on Neural Information Processing Systems."},{"key":"e_1_3_4_263_2","first-page":"15","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Yang Yaodong","year":"2024","unstructured":"Yaodong Yang, Guangyong Chen, Jianye Hao, and Pheng Ann Heng. 2024. Sample-efficient multiagent reinforcement learning with reset replay. In Proceedings of the 41st International Conference on Machine Learning. 15 pages."},{"key":"e_1_3_4_264_2","first-page":"7074","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yang Yiding","year":"2020","unstructured":"Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, and Xinchao Wang. 2020. Distilling knowledge from graph convolutional networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 7074\u20137083."},{"issue":"4","key":"e_1_3_4_265_2","doi-asserted-by":"crossref","first-page":"6656","DOI":"10.1609\/aaai.v34i04.6142","article-title":"Graph few-shot learning via knowledge transfer","volume":"34","author":"Yao Huaxiu","year":"2020","unstructured":"Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, and Zhenhui Li. 2020. Graph few-shot learning via knowledge transfer. Proceedings of the AAAI Conference on Artificial Intelligence 34, 4 (2020), 6656\u20136663.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_4_266_2","volume-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","author":"Ye Haoran","year":"2024","unstructured":"Haoran Ye, Jiarui Wang, Zhiguang Cao, Federico Berto, Chuanbo Hua, Haeyeon Kim, Jinkyoo Park, and Guojie Song. 2024. ReEvo: Large language models as hyper-heuristics with reflective evolution. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_4_267_2","first-page":"8808","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Ye Han-Jia","year":"2020","unstructured":"Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha. 2020. Few-shot learning via embedding adaptation with set-to-set functions. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8808\u20138817."},{"key":"e_1_3_4_268_2","first-page":"25476","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Ye Weirui","year":"2021","unstructured":"Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao. 2021. Mastering atari games with limited data. In Proceedings of the Advances in Neural Information Processing Systems. 25476\u201325488."},{"issue":"3","key":"e_1_3_4_269_2","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1609\/aaai.v36i3.20226","article-title":"Hybrid graph neural networks for few-shot learning","volume":"36","author":"Yu Tianyuan","year":"2022","unstructured":"Tianyuan Yu, Sen He, Yi-Zhe Song, and Tao Xiang. 2022. Hybrid graph neural networks for few-shot learning. Proceedings of the AAAI Conference on Artificial Intelligence 36, 3 (2022), 3179\u20133187.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_4_270_2","first-page":"14129","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Yu Tianhe","year":"2020","unstructured":"Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma. 2020. MOPO: Model-based Offline Policy Optimization. In Proceedings of the Advances in Neural Information Processing Systems. 14129\u201314142."},{"key":"e_1_3_4_271_2","first-page":"12856","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yu Zhongjie","year":"2020","unstructured":"Zhongjie Yu, Lin Chen, Zhongwei Cheng, and Jiebo Luo. 2020. Transmatch: A transfer-learning scheme for semi-supervised few-shot learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 12856\u201312864."},{"key":"e_1_3_4_272_2","first-page":"44367","volume-title":"Proceedings of the International Conference on Representation Learning","author":"Yuan Haoqi","year":"2024","unstructured":"Haoqi Yuan, Zhancun Mu, Feiyang Xie, and Zongqing Lu. 2024. Pre-training goal-based models for sample-efficient reinforcement learning. In Proceedings of the International Conference on Representation Learning. 44367\u201344388."},{"key":"e_1_3_4_273_2","first-page":"13829","volume-title":"Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Yue Xiangyu","year":"2021","unstructured":"Xiangyu Yue, Zangwei Zheng, Shanghang Zhang, Yang Gao, Trevor Darrell, Kurt Keutzer, and Alberto Sangiovanni Vincentelli. 2021. Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation. In Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 13829\u201313839."},{"key":"e_1_3_4_274_2","first-page":"1153","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zang Xinshi","year":"2020","unstructured":"Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, and Zhenhui Li. 2020. Metalight: Value-based meta-reinforcement learning for traffic signal control. In Proceedings of the AAAI Conference on Artificial Intelligence. 1153\u20131160."},{"key":"e_1_3_4_275_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020. GraphSAINT: Graph sampling based inductive learning method. In Proceedings of the International Conference on Learning Representations."},{"issue":"182","key":"e_1_3_4_276_2","first-page":"1","article-title":"Hierarchical decision making based on structural information principles","volume":"26","author":"Zeng Xianghua","year":"2025","unstructured":"Xianghua Zeng, Hao Peng, Dingli Su, and Angsheng Li. 2025. Hierarchical decision making based on structural information principles. Journal of Machine Learning Research 26, 182 (2025), 1\u201355.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_4_277_2","volume-title":"Proceedings of the 38th Annual Conference on Neural Information Processing Systems","author":"Zhai Yuexiang","year":"2024","unstructured":"Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Yifei Zhou, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, et\u00a0al. 2024. Fine-tuning large vision-language models as decision-making agents via reinforcement learning. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_4_278_2","first-page":"5662","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI-22","author":"Zhang Chuxu","year":"2022","unstructured":"Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, and Huan Liu. 2022. Few-shot learning on graphs. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI-22. 5662\u20135669."},{"key":"e_1_3_4_279_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhang Jesse","year":"2021","unstructured":"Jesse Zhang, Haonan Yu, and Wei Xu. 2021. Hierarchical reinforcement learning by discovering intrinsic options. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_280_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhang Shichang","year":"2022","unstructured":"Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah. 2022. Graph-less neural networks: Teaching old MLPs new tricks via distillation. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_4_281_2","first-page":"1665","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI-22","author":"Zhang Wenyu","year":"2022","unstructured":"Wenyu Zhang, Li Shen, Wanyue Zhang, and Chuan-Sheng Foo. 2022. Few-shot adaptation of pre-trained networks for domain shift. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI-22. 1665\u20131671."},{"key":"e_1_3_4_282_2","volume-title":"Findings of the Association for Computational Linguistics: ACL 2025","author":"Zhou Huachi","year":"2025","unstructured":"Huachi Zhou, Jiahe Du, Chuang Zhou, Chang Yang, Yilin Xiao, Yuxuan Xie, and Xiao Huang. 2025. Each graph is a new language: Graph Learning with LLMs. In Findings of the Association for Computational Linguistics: ACL 2025."},{"key":"e_1_3_4_283_2","doi-asserted-by":"crossref","first-page":"14707","DOI":"10.1109\/CVPR52733.2024.01393","volume-title":"Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Zhou Xin","year":"2024","unstructured":"Xin Zhou, Dingkang Liang, Wei Xu, Xingkui Zhu, Yihan Xu, Zhikang Zou, and Xiang Bai. 2024. Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis. In Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14707\u201314717."},{"key":"e_1_3_4_284_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Zhu Ronghang","year":"2023","unstructured":"Ronghang Zhu, Ronghang Zhu, Xiang Yu, and Sheng Li. 2023. Progressive mix-up for few-shot supervised multi-source domain transfer. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_4_285_2","volume-title":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","author":"Zhuang Fuzhen","year":"2015","unstructured":"Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, and Qing He. 2015. Supervised representation learning: Transfer learning with deep autoencoders. In Proceedings of the 24th International Joint Conference on Artificial Intelligence."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3773075","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T14:07:50Z","timestamp":1765202870000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3773075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":284,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,4,30]]}},"alternative-id":["10.1145\/3773075"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1145\/3773075","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,8]]},"assertion":[{"value":"2023-11-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}