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ORCIDOlivier Bachem
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- affiliation: ETH Zurich, Department of Computer Science, Switzerland
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Journal Articles
- 2020
[j1]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)
Conference and Workshop Papers
- 2024
- [c37]Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem:
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR 2024 - [c36]Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli:
MusicRL: Aligning Music Generation to Human Preferences. ICML 2024 - [c35]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. ICML 2024 - [c34]Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret:
WARM: On the Benefits of Weight Averaged Reward Models. ICML 2024 - 2023
- [c33]Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor:
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. ACL (1) 2023: 6252-6272 - 2022
- [c32]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-exploration. AAAI 2022: 8106-8114 - [c31]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado
, Pablo Samuel Castro, Nicolas Le Roux:
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS 2022: 8619-8649 - [c30]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. AAMAS 2022: 489-497 - [c29]Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann:
Decoding a Neural Retriever's Latent Space for Query Suggestion. EMNLP 2022: 8786-8804 - [c28]Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
The Role of Pretrained Representations for the OOD Generalization of RL Agents. ICLR 2022 - 2021
- [c27]Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. ICLR 2021 - [c26]Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphaël Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin:
Hyperparameter Selection for Imitation Learning. ICML 2021: 4511-4522 - [c25]C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem:
Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. NeurIPS Datasets and Benchmarks 2021 - [c24]Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz:
What Matters for Adversarial Imitation Learning? NeurIPS 2021: 14656-14668 - 2020
- [c23]Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly:
Google Research Football: A Novel Reinforcement Learning Environment. AAAI 2020: 4501-4510 - [c22]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 - [c21]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Precision-Recall Curves Using Information Divergence Frontiers. AISTATS 2020: 2550-2559 - [c20]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variations Using Few Labels. ICLR 2020 - [c19]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. ICML 2020: 6348-6359 - [c18]Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen:
Automatic Shortcut Removal for Self-Supervised Representation Learning. ICML 2020: 6927-6937 - 2019
- [c17]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 - [c16]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 - [c15]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. ICML 2019: 4183-4192 - [c14]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS 2019: 14222-14235 - [c13]Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. NeurIPS 2019: 14584-14597 - [c12]Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. NeurIPS 2019: 15714-15725 - 2018
- [c11]Olivier Bachem, Mario Lucic, Silvio Lattanzi:
One-shot Coresets: The Case of k-Clustering. AISTATS 2018: 784-792 - [c10]Olivier Bachem, Mario Lucic, Andreas Krause:
Scalable k -Means Clustering via Lightweight Coresets. KDD 2018: 1119-1127 - [c9]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. NeurIPS 2018: 5234-5243 - 2017
- [c8]Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Uniform Deviation Bounds for k-Means Clustering. ICML 2017: 283-291 - [c7]Olivier Bachem, Mario Lucic, Andreas Krause:
Distributed and Provably Good Seedings for k-Means in Constant Rounds. ICML 2017: 292-300 - 2016
- [c6]Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Approximate K-Means++ in Sublinear Time. AAAI 2016: 1459-1467 - [c5]Mario Lucic, Olivier Bachem, Andreas Krause:
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. AISTATS 2016: 1-9 - [c4]Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause:
Horizontally Scalable Submodular Maximization. ICML 2016: 2981-2989 - [c3]Mario Lucic, Olivier Bachem, Andreas Krause:
Linear-Time Outlier Detection via Sensitivity. IJCAI 2016: 1795-1801 - [c2]Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause:
Fast and Provably Good Seedings for k-Means. NIPS 2016: 55-63 - 2015
- [c1]Olivier Bachem, Mario Lucic, Andreas Krause:
Coresets for Nonparametric Estimation - the Case of DP-Means. ICML 2015: 209-217
Informal and Other Publications
- 2024
- [i45]Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret:
WARM: On the Benefits of Weight Averaged Reward Models. CoRR abs/2401.12187 (2024) - [i44]Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli:
MusicRL: Aligning Music Generation to Human Preferences. CoRR abs/2402.04229 (2024) - [i43]Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz GUStavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas:
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR abs/2404.07839 (2024) - [i42]Alexandre Ramé, Johan Ferret, Nino Vieillard, Robert Dadashi, Léonard Hussenot, Pierre-Louis Cedoz, Pier Giuseppe Sessa, Sertan Girgin, Arthur Douillard, Olivier Bachem:
WARP: On the Benefits of Weight Averaged Rewarded Policies. CoRR abs/2406.16768 (2024) - [i41]Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Ramé, Bobak Shahriari, Sarah Perrin, Abe Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos, Amélie Héliou, Aliaksei Severyn, Matt Hoffman, Nikola Momchev, Olivier Bachem:
BOND: Aligning LLMs with Best-of-N Distillation. CoRR abs/2407.14622 (2024) - [i40]Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent:
Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. CoRR abs/2407.15762 (2024) - [i39]Morgane Rivière, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin, Nikola Momchev, Matt Hoffman, Shantanu Thakoor, Jean-Bastien Grill, Behnam Neyshabur, Olivier Bachem, Alanna Walton, Aliaksei Severyn, Alicia Parrish, Aliya Ahmad, Allen Hutchison, Alvin Abdagic, Amanda Carl, Amy Shen, Andy Brock, Andy Coenen, Anthony Laforge, Antonia Paterson, Ben Bastian, Bilal Piot, Bo Wu, Brandon Royal, Charlie Chen, Chintu Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozinska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Hadi Hashemi, Hanna Klimczak-Plucinska, Harleen Batra, Harsh Dhand, Ivan Nardini, Jacinda Mein, Jack Zhou, James Svensson, Jeff Stanway, Jetha Chan, Jin Peng Zhou, Joana Carrasqueira, Joana Iljazi, Jocelyn Becker, Joe Fernandez, Joost van Amersfoort, Josh Gordon, Josh Lipschultz, Josh Newlan, Ju-yeong Ji, Kareem Mohamed, Kartikeya Badola, Kat Black, Katie Millican, Keelin McDonell, Kelvin Nguyen, Kiranbir Sodhia, Kish Greene, Lars Lowe Sjösund, Lauren Usui, Laurent Sifre, Lena Heuermann, Leticia Lago, Lilly McNealus:
Gemma 2: Improving Open Language Models at a Practical Size. CoRR abs/2408.00118 (2024) - [i38]Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jorg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Maria Elisabeth Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin A. Riedmiller:
Imitating Language via Scalable Inverse Reinforcement Learning. CoRR abs/2409.01369 (2024) - 2023
- [i37]Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor:
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. CoRR abs/2306.00186 (2023) - [i36]Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem:
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR abs/2306.13649 (2023) - [i35]Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Zhaohan Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot:
Nash Learning from Human Feedback. CoRR abs/2312.00886 (2023) - 2022
- [i34]Geoffrey Cideron, Sertan Girgin, Anton Raichuk, Olivier Pietquin, Olivier Bachem, Léonard Hussenot:
vec2text with Round-Trip Translations. CoRR abs/2209.06792 (2022) - [i33]Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann:
Decoding a Neural Retriever's Latent Space for Query Suggestion. CoRR abs/2210.12084 (2022) - [i32]Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem:
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. CoRR abs/2211.03521 (2022) - 2021
- [i31]Baris Sumengen, Anand Rajagopalan, Gui Citovsky, David Simcha, Olivier Bachem, Pradipta Mitra, Sam Blasiak, Mason Liang, Sanjiv Kumar:
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets. CoRR abs/2105.11653 (2021) - [i30]Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Lukasz Stafiniak, Sertan Girgin, Raphaël Marinier, Nikola Momchev, Sabela Ramos, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin:
Hyperparameter Selection for Imitation Learning. CoRR abs/2105.12034 (2021) - [i29]Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz:
What Matters for Adversarial Imitation Learning? CoRR abs/2106.00672 (2021) - [i28]Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin:
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. CoRR abs/2106.03787 (2021) - [i27]Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist:
Offline Reinforcement Learning as Anti-Exploration. CoRR abs/2106.06431 (2021) - [i26]C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem:
Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. CoRR abs/2106.13281 (2021) - [i25]Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter V. Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. CoRR abs/2107.05686 (2021) - [i24]Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux:
A functional mirror ascent view of policy gradient methods with function approximation. CoRR abs/2108.05828 (2021) - [i23]Shixiang Shane Gu, Manfred Diaz, C. Daniel Freeman, Hiroki Furuta, Seyed Kamyar Seyed Ghasemipour, Anton Raichuk, Byron David, Erik Frey, Erwin Coumans, Olivier Bachem:
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization. CoRR abs/2110.04686 (2021) - 2020
- [i22]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. CoRR abs/2002.02886 (2020) - [i21]Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen:
Automatic Shortcut Removal for Self-Supervised Representation Learning. CoRR abs/2002.08822 (2020) - [i20]Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem:
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. CoRR abs/2006.05990 (2020) - [i19]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) - [i18]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
- [i17]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. CoRR abs/1903.02271 (2019) - [i16]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variation Using Few Labels. CoRR abs/1905.01258 (2019) - [i15]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Evaluating Generative Models Using Divergence Frontiers. CoRR abs/1905.10768 (2019) - [i14]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? CoRR abs/1905.12506 (2019) - [i13]Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. CoRR abs/1905.13662 (2019) - [i12]Muhammad Waleed Gondal, Manuel Wüthrich, Ðorðe Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. CoRR abs/1906.03292 (2019) - [i11]Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly:
Google Research Football: A Novel Reinforcement Learning Environment. CoRR abs/1907.11180 (2019) - [i10]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
- [i9]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. CoRR abs/1806.00035 (2018) - [i8]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) - [i7]Michael Tschannen, Olivier Bachem, Mario Lucic:
Recent Advances in Autoencoder-Based Representation Learning. CoRR abs/1812.05069 (2018) - 2017
- [i6]Olivier Bachem, Mario Lucic, Andreas Krause:
Scalable and Distributed Clustering via Lightweight Coresets. CoRR abs/1702.08248 (2017) - [i5]Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause:
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means. CoRR abs/1702.08249 (2017) - [i4]Olivier Bachem, Mario Lucic, Silvio Lattanzi:
One-Shot Coresets: The Case of k-Clustering. CoRR abs/1711.09649 (2017) - 2016
- [i3]Mario Lucic, Olivier Bachem, Andreas Krause:
Linear-time Outlier Detection via Sensitivity. CoRR abs/1605.00519 (2016) - [i2]Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause:
Horizontally Scalable Submodular Maximization. CoRR abs/1605.09619 (2016) - 2015
- [i1]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)
Coauthor Index
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