Bachem, Olivier

38 publications

ICLR 2025 BOND: Aligning LLMs with Best-of-N Distillation Pier Giuseppe Sessa, Robert Dadashi-Tazehozi, Leonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Rame, Bobak Shahriari, Sarah Perrin, Abram L. Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos Garea, Amélie Héliou, Aliaksei Severyn, Matthew Hoffman, Nikola Momchev, Olivier Bachem

ICLR 2025 Diversity-Rewarded CFG Distillation Geoffrey Cideron, Andrea Agostinelli, Johan Ferret, Sertan Girgin, Romuald Elie, Olivier Bachem, Sarah Perrin, Alexandre Rame

NeurIPSW 2024 Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Avinava Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent

NeurIPS 2024 Imitating Language via Scalable Inverse Reinforcement Learning Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Maria Elisabeth Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin Riedmiller

ICML 2024 MusicRL: Aligning Music Generation to Human Preferences Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian Mcwilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Leonard Hussenot, Neil Zeghidour, Andrea Agostinelli

ICML 2024 Nash Learning from Human Feedback Remi 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

ICLR 2024 On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem

ICML 2024 WARM: On the Benefits of Weight Averaged Reward Models Alexandre Rame, Nino Vieillard, Leonard Hussenot, Robert Dadashi-Tazehozi, Geoffrey Cideron, Olivier Bachem, Johan Ferret

NeurIPSW 2023 On the Importance of Data Collection for Training General Goal-Reaching Policies. Alexis D. Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem

AISTATS 2022 A General Class of Surrogate Functions for Stable and Efficient Reinforcement Learning Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux

AAAI 2022 Offline Reinforcement Learning as Anti-Exploration Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist

ICLR 2022 The Role of Pretrained Representations for the OOD Generalization of RL Agents Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

ICML 2021 Hyperparameter Selection for Imitation Learning Léonard Hussenot, Marcin Andrychowicz, Damien Vincent, Robert Dadashi, Anton Raichuk, Sabela Ramos, Nikola Momchev, Sertan Girgin, Raphael Marinier, Lukasz Stafiniak, Manu Orsini, Olivier Bachem, Matthieu Geist, Olivier Pietquin

ICMLW 2021 Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

NeurIPS 2021 What Matters for Adversarial Imitation Learning? Manu Orsini, Anton Raichuk, Leonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz

ICLR 2021 What Matters for On-Policy Deep Actor-Critic Methods? a Large-Scale Study Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Leonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem

AAAI 2020 A Commentary on the Unsupervised Learning of Disentangled Representations Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

JMLR 2020 A Sober Look at the Unsupervised Learning of Disentangled Representations and Their Evaluation Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

ICML 2020 Automatic Shortcut Removal for Self-Supervised Representation Learning Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen

ICLR 2020 Disentangling Factors of Variations Using Few Labels Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem

AAAI 2020 Google Research Football: A Novel Reinforcement Learning Environment Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

AISTATS 2020 Precision-Recall Curves Using Information Divergence Frontiers Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly

ICML 2020 Weakly-Supervised Disentanglement Without Compromises Francesco Locatello, Ben Poole, Gunnar Raetsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen

NeurIPS 2019 Are Disentangled Representations Helpful for Abstract Visual Reasoning? Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem

ICML 2019 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

ICLRW 2019 Disentangling Factors of Variations Using Few Labels Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar R¨¨ätsch, Bernhard Schölkopf, Olivier Bachem

NeurIPS 2019 On the Fairness of Disentangled Representations Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

NeurIPS 2019 On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

NeurIPS 2018 Assessing Generative Models via Precision and Recall Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

AISTATS 2018 One-Shot Coresets: The Case of K-Clustering Olivier Bachem, Mario Lucic, Silvio Lattanzi

ICML 2017 Distributed and Provably Good Seedings for K-Means in Constant Rounds Olivier Bachem, Mario Lucic, Andreas Krause

ICML 2017 Uniform Deviation Bounds for K-Means Clustering Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause

AAAI 2016 Approximate K-Means++ in Sublinear Time Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause

NeurIPS 2016 Fast and Provably Good Seedings for K-Means Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause

ICML 2016 Horizontally Scalable Submodular Maximization Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause

IJCAI 2016 Linear-Time Outlier Detection via Sensitivity Mario Lucic, Olivier Bachem, Andreas Krause

AISTATS 2016 Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures Mario Lucic, Olivier Bachem, Andreas Krause

ICML 2015 Coresets for Nonparametric Estimation - The Case of DP-Means Olivier Bachem, Mario Lucic, Andreas Krause