Olivier Bachem

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 76

Top Topics

Policy Gradient Methods

Reinforcement Learning

Nonparametric Estimation

Top Venues

Publications

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 (2024)
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)
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 (2024)
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 (2024)
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 (2024)
Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
WARM: On the Benefits of Weight Averaged Reward Models. CoRR (2024)
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 (2024)
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 (2023)
Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem
GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR (2023)
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 (2023)
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)
Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem
C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. CoRR (2022)
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)
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
Offline Reinforcement Learning as Anti-exploration. AAAI (2022)
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 (2022)
Geoffrey Cideron, Sertan Girgin, Anton Raichuk, Olivier Pietquin, Olivier Bachem, Léonard Hussenot
vec2text with Round-Trip Translations. CoRR (2022)
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)
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)
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)
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 (2021)
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)
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 (2021)
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)
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 (2021)
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 (2021)
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 (2021)
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 (2021)
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 (2021)
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 (2021)
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
Offline Reinforcement Learning as Anti-Exploration. CoRR (2021)
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)
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)
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 (2020)
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 (2020)
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 (2020)
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
Weakly-Supervised Disentanglement Without Compromises. ICML (2020)
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
Precision-Recall Curves Using Information Divergence Frontiers. AISTATS (2020)
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)
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)
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
Disentangling Factors of Variations Using Few Labels. ICLR (2020)
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
Weakly-Supervised Disentanglement Without Compromises. CoRR (2020)
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
Automatic Shortcut Removal for Self-Supervised Representation Learning. CoRR (2020)
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
Automatic Shortcut Removal for Self-Supervised Representation Learning. ICML (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR (2020)
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
Evaluating Generative Models Using Divergence Frontiers. CoRR (2019)
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem
Are Disentangled Representations Helpful for Abstract Visual Reasoning? CoRR (2019)
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)
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 (2019)
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 (2019)
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
High-Fidelity Image Generation With Fewer Labels. ICML (2019)
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
High-Fidelity Image Generation With Fewer Labels. CoRR (2019)
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 (2019)
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)
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
Disentangling Factors of Variation Using Few Labels. CoRR (2019)
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
On the Fairness of Disentangled Representations. NeurIPS (2019)
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem
Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS (2019)
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
On the Fairness of Disentangled Representations. CoRR (2019)
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)
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Assessing Generative Models via Precision and Recall. CoRR (2018)
Olivier Bachem, Mario Lucic, Silvio Lattanzi
One-shot Coresets: The Case of k-Clustering. AISTATS (2018)
Michael Tschannen, Olivier Bachem, Mario Lucic
Recent Advances in Autoencoder-Based Representation Learning. CoRR (2018)
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Assessing Generative Models via Precision and Recall. NeurIPS (2018)
Olivier Bachem, Mario Lucic, Andreas Krause
Scalable k -Means Clustering via Lightweight Coresets. KDD (2018)
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR (2018)
Olivier Bachem, Mario Lucic, Silvio Lattanzi
One-Shot Coresets: The Case of k-Clustering. CoRR (2017)
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means. CoRR (2017)
Olivier Bachem, Mario Lucic, Andreas Krause
Distributed and Provably Good Seedings for k-Means in Constant Rounds. ICML (2017)
Olivier Bachem, Mario Lucic, Andreas Krause
Scalable and Distributed Clustering via Lightweight Coresets. CoRR (2017)
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
Uniform Deviation Bounds for k-Means Clustering. ICML (2017)
Mario Lucic, Olivier Bachem, Andreas Krause
Linear-Time Outlier Detection via Sensitivity. IJCAI (2016)
Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause
Fast and Provably Good Seedings for k-Means. NIPS (2016)
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
Horizontally Scalable Submodular Maximization. CoRR (2016)
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
Approximate K-Means++ in Sublinear Time. AAAI (2016)
Mario Lucic, Olivier Bachem, Andreas Krause
Linear-time Outlier Detection via Sensitivity. CoRR (2016)
Mario Lucic, Olivier Bachem, Andreas Krause
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. AISTATS (2016)
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
Horizontally Scalable Submodular Maximization. ICML (2016)
Mario Lucic, Olivier Bachem, Andreas Krause
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. CoRR (2015)
Olivier Bachem, Mario Lucic, Andreas Krause
Coresets for Nonparametric Estimation - the Case of DP-Means. ICML (2015)