Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy.

Sina Ghiassian

Kamil Ciosek Nicolò Felicioni Sina Ghiassian Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy. 2025 2025 Trans. Mach. Learn. Res. https://openreview.net/forum?id=j2N2RuNdbC db/journals/tmlr/tmlr2025.html#CiosekFG25 streams/journals/tmlr

Arash Tavakoli Sina Ghiassian Nemanja Rakicevic Learning in complex action spaces without policy gradients. 2025 2025 Trans. Mach. Learn. Res. https://openreview.net/forum?id=nOL9M6D4oM db/journals/tmlr/tmlr2025.html#TavakoliGR25

Sina Ghiassian Banafsheh Rafiee Richard S. Sutton Off-Policy Prediction Learning: An Empirical Study of Online Algorithms. 4477-4491 2025 March 36 IEEE Trans. Neural Networks Learn. Syst. 3 https://doi.org/10.1109/TNNLS.2024.3373749 db/journals/tnn/tnn36.html#GhiassianRS25 streams/journals/tnn

Kamil Ciosek Nicolò Felicioni Sina Ghiassian Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy. 2025 April abs/2504.03579 CoRR https://doi.org/10.48550/arXiv.2504.03579 db/journals/corr/corr2504.html#abs-2504-03579 streams/journals/corr

Kamil Ciosek Nicolò Felicioni Sina Ghiassian Juan Elenter Litwin Francesco Tonolini David Gustaffson Eva Garcia Martin Carmen Barcena Gonzales Raphaëlle Bertrand-Lalo Measuring Uncertainty Calibration. 2025 December abs/2512.13872 CoRR https://doi.org/10.48550/arXiv.2512.13872 db/journals/corr/corr2512.html#abs-2512-13872 streams/journals/corr

Nicolò Felicioni Lucas Maystre Sina Ghiassian Kamil Ciosek On the Importance of Uncertainty in Decision-Making with Large Language Models. 2024 2024 Trans. Mach. Learn. Res. https://openreview.net/forum?id=YfPzUX6DdO db/journals/tmlr/tmlr2024.html#FelicioniMGC24

Zhenwen Dai Federico Tomasi Sina Ghiassian In-context Exploration-Exploitation for Reinforcement Learning. 2024 ICLR https://openreview.net/forum?id=uIKZSStON3 conf/iclr/2024 db/conf/iclr/iclr2024.html#DaiTG24

Zhenwen Dai Federico Tomasi Sina Ghiassian In-context Exploration-Exploitation for Reinforcement Learning. 2024 abs/2403.06826 CoRR https://doi.org/10.48550/arXiv.2403.06826 db/journals/corr/corr2403.html#abs-2403-06826

Nicolò Felicioni Lucas Maystre Sina Ghiassian Kamil Ciosek On the Importance of Uncertainty in Decision-Making with Large Language Models. 2024 abs/2404.02649 CoRR https://doi.org/10.48550/arXiv.2404.02649 db/journals/corr/corr2404.html#abs-2404-02649

Arsalan Sharifnassab Sina Ghiassian Saber Salehkaleybar Surya Kanoria Dale Schuurmans Soft Preference Optimization: Aligning Language Models to Expert Distributions. 2024 abs/2405.00747 CoRR https://doi.org/10.48550/arXiv.2405.00747 db/journals/corr/corr2405.html#abs-2405-00747

Arash Tavakoli Sina Ghiassian Nemanja Rakicevic Learning in complex action spaces without policy gradients. 2024 abs/2410.06317 CoRR https://doi.org/10.48550/arXiv.2410.06317 db/journals/corr/corr2410.html#abs-2410-06317 streams/journals/corr

Banafsheh Rafiee Zaheer Abbas Sina Ghiassian Raksha Kumaraswamy Richard S. Sutton Elliot A. Ludvig Adam White 0001 From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. 3-19 2023 February 31 Adapt. Behav. 1 https://doi.org/10.1177/10597123221085039 https://www.wikidata.org/entity/Q114063809 db/journals/adb/adb31.html#RafieeAGKSLW23

Banafsheh Rafiee Sina Ghiassian Jun Jin 0001 Richard S. Sutton Jun Luo 0009 Adam White 0001 Auxiliary task discovery through generate-and-test. 703-714 2023 CoLLAs https://proceedings.mlr.press/v232/rafiee23a.html conf/collas/2023 db/conf/collas/collas2023.html#RafieeG0S0023

Eric Graves 0002 Sina Ghiassian Importance Sampling Placement in Off-Policy Temporal-Difference Methods. 2022 abs/2203.10172 CoRR https://doi.org/10.48550/arXiv.2203.10172 db/journals/corr/corr2203.html#abs-2203-10172

Banafsheh Rafiee Sina Ghiassian Jun Jin 0001 Richard S. Sutton Jun Luo 0009 Adam White 0001 Auxiliary task discovery through generate-and-test. 2022 abs/2210.14361 CoRR https://doi.org/10.48550/arXiv.2210.14361 db/journals/corr/corr2210.html#abs-2210-14361

Dylan R. Ashley Sina Ghiassian Richard S. Sutton Does Standard Backpropagation Forget Less Catastrophically Than Adam? 2021 abs/2102.07686 CoRR https://arxiv.org/abs/2102.07686 db/journals/corr/corr2102.html#abs-2102-07686

Andrew Patterson Adam White 0001 Sina Ghiassian Martha White A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. 2021 abs/2104.13844 CoRR https://arxiv.org/abs/2104.13844 db/journals/corr/corr2104.html#abs-2104-13844

Sina Ghiassian Richard S. Sutton An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task. 2021 abs/2106.00922 CoRR https://arxiv.org/abs/2106.00922 db/journals/corr/corr2106.html#abs-2106-00922

Sina Ghiassian Richard S. Sutton An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment. 2021 abs/2109.05110 CoRR https://arxiv.org/abs/2109.05110 db/journals/corr/corr2109.html#abs-2109-05110

Sina Ghiassian Banafsheh Rafiee Yat Long Lo Adam White 0001 Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. 438-446 2020 AAMAS https://dl.acm.org/doi/10.5555/3398761.3398816 https://www.ifaamas.org/Proceedings/aamas2020/pdfs/p438.pdf conf/atal/2020 db/conf/atal/aamas2020.html#GhiassianRLW20 Sina Ghiassian Andrew Patterson Shivam Garg 0006 Dhawal Gupta Adam White 0001 Martha White Gradient Temporal-Difference Learning with Regularized Corrections. 3524-3534 2020 ICML http://proceedings.mlr.press/v119/ghiassian20a.html conf/icml/2020 db/conf/icml/icml2020.html#GhiassianP0GWW20

Sina Ghiassian Banafsheh Rafiee Yat Long Lo Adam White 0001 Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. 2020 abs/2003.07417 CoRR https://arxiv.org/abs/2003.07417 db/journals/corr/corr2003.html#abs-2003-07417

Sina Ghiassian Andrew Patterson Shivam Garg 0006 Dhawal Gupta Adam White 0001 Martha White Gradient Temporal-Difference Learning with Regularized Corrections. 2020 abs/2007.00611 CoRR https://arxiv.org/abs/2007.00611 db/journals/corr/corr2007.html#abs-2007-00611

Banafsheh Rafiee Sina Ghiassian Adam White 0001 Richard S. Sutton Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. 332-340 2019 AAMAS http://dl.acm.org/citation.cfm?id=3331711 conf/atal/2019 db/conf/atal/aamas2019.html#RafieeGWS19

Xiang Gu 0002 Sina Ghiassian Richard S. Sutton Should All Temporal Difference Learning Use Emphasis? 2019 abs/1903.00194 CoRR http://arxiv.org/abs/1903.00194 db/journals/corr/corr1903.html#abs-1903-00194

Yat Long Lo Sina Ghiassian Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps. 2019 abs/1910.13213 CoRR http://arxiv.org/abs/1910.13213 db/journals/corr/corr1910.html#abs-1910-13213

Sina Ghiassian Huizhen Yu Banafsheh Rafiee Richard S. Sutton Two geometric input transformation methods for fast online reinforcement learning with neural nets. 2018 abs/1805.07476 CoRR http://arxiv.org/abs/1805.07476 db/journals/corr/corr1805.html#abs-1805-07476

Sina Ghiassian Andrew Patterson Martha White Richard S. Sutton Adam White 0001 Online Off-policy Prediction. 2018 abs/1811.02597 CoRR http://arxiv.org/abs/1811.02597 db/journals/corr/corr1811.html#abs-1811-02597

Sina Ghiassian Banafsheh Rafiee Richard S. Sutton A First Empirical Study of Emphatic Temporal Difference Learning. 2017 abs/1705.04185 CoRR http://arxiv.org/abs/1705.04185 db/journals/corr/corr1705.html#GhiassianRS17

Zaheer Abbas Dylan R. Ashley Raphaëlle Bertrand-Lalo Kamil Ciosek Zhenwen Dai Nicolò Felicioni Shivam Garg 0006 Carmen Barcena Gonzales Eric Graves 0002 Xiang Gu 0002 Dhawal Gupta David Gustaffson Jun Jin 0001 Surya Kanoria Raksha Kumaraswamy Juan Elenter Litwin Yat Long Lo Elliot A. Ludvig Jun Luo 0009 Eva Garcia Martin Lucas Maystre Andrew Patterson Banafsheh Rafiee Nemanja Rakicevic Saber Salehkaleybar Dale Schuurmans Arsalan Sharifnassab Richard S. Sutton Arash Tavakoli Federico Tomasi Francesco Tonolini Adam White 0001 Martha White Huizhen Yu