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ORCIDMario Lucic
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Publications
- 2020
[j2]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. 21: 209:1-209:62 (2020)- [c27]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI 2020: 13681-13684 - [c26]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Precision-Recall Curves Using Information Divergence Frontiers. AISTATS 2020: 2550-2559 - [i28]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR abs/2007.14184 (2020) - [i25]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR abs/2010.14766 (2020) - 2019
- [c21]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR 2019 - [c19]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML 2019: 4114-4124 - [c18]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. ICML 2019: 4183-4192 - [i23]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. CoRR abs/1903.02271 (2019) - [i22]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Evaluating Generative Models Using Divergence Frontiers. CoRR abs/1905.10768 (2019) - [i20]Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann
, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby:
The Visual Task Adaptation Benchmark. CoRR abs/1910.04867 (2019) - 2018
- [c17]Olivier Bachem, Mario Lucic, Silvio Lattanzi:
One-shot Coresets: The Case of k-Clustering. AISTATS 2018: 784-792 - [c16]Olivier Bachem, Mario Lucic, Andreas Krause:
Scalable k -Means Clustering via Lightweight Coresets. KDD 2018: 1119-1127 - [c14]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. NeurIPS 2018: 5234-5243 - [i16]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. CoRR abs/1806.00035 (2018) - [i12]Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR abs/1811.12359 (2018) - [i11]Michael Tschannen, Olivier Bachem, Mario Lucic:
Recent Advances in Autoencoder-Based Representation Learning. CoRR abs/1812.05069 (2018) - 2017
- [c12]Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Uniform Deviation Bounds for k-Means Clustering. ICML 2017: 283-291 - [c11]Olivier Bachem, Mario Lucic, Andreas Krause:
Distributed and Provably Good Seedings for k-Means in Constant Rounds. ICML 2017: 292-300 - [i10]Olivier Bachem, Mario Lucic, Andreas Krause:
Scalable and Distributed Clustering via Lightweight Coresets. CoRR abs/1702.08248 (2017) - [i9]Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means. CoRR abs/1702.08249 (2017) - [i7]Olivier Bachem, Mario Lucic, Silvio Lattanzi:
One-Shot Coresets: The Case of k-Clustering. CoRR abs/1711.09649 (2017) - 2016
- [c9]Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Approximate K-Means++ in Sublinear Time. AAAI 2016: 1459-1467 - [c8]Mario Lucic, Olivier Bachem, Andreas Krause:
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. AISTATS 2016: 1-9 - [c6]Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause:
Horizontally Scalable Submodular Maximization. ICML 2016: 2981-2989 - [c5]Mario Lucic, Olivier Bachem, Andreas Krause:
Linear-Time Outlier Detection via Sensitivity. IJCAI 2016: 1795-1801 - [c4]Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause:
Fast and Provably Good Seedings for k-Means. NIPS 2016: 55-63 - [i5]Mario Lucic, Olivier Bachem, Andreas Krause:
Linear-time Outlier Detection via Sensitivity. CoRR abs/1605.00519 (2016) - [i3]Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause:
Horizontally Scalable Submodular Maximization. CoRR abs/1605.09619 (2016) - 2015
- [c2]Olivier Bachem, Mario Lucic, Andreas Krause:
Coresets for Nonparametric Estimation - the Case of DP-Means. ICML 2015: 209-217 - [i2]Mario Lucic, Olivier Bachem, Andreas Krause:
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. CoRR abs/1508.05243 (2015)
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