Olivier Bachem

36 papers · 2015–2025 · 9 conferences · across top CS/AI conferences

Achievements

+12 more ↓

🏃 Academic Marathon (10) 🐝 Cross-Pollinator (13) 🌍 Conference Polyglot (9) 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (10)

🌈 Renaissance Researcher (10) 🗺️ Taxonomy Completionist (53) 🧭 Keyword Pioneer 👥 Mega-Team (20) 👑 Triple Crown 🏆 Grand Slam 🤝 Dynamic Duo (13) 💎 Century Club (36) 🗃️ Keyword Collector (114) 🔥 Unstoppable (11) ❓ The Questioner (3) ⚡ Prolific Year (6)

Conferences

ICML (12) NIPS (7) ICLR (6) AISTATS (4) AAAI (2) EMNLP (2) ACL (1) IJCAI (1) JMLR (1)

Top co-authors

Mario Lucic (13) Matthieu Geist (10) Leonard Hussenot (9) Sertan Girgin (9) Francesco Locatello (8) Bernhard Schölkopf (7) Andreas Krause (7) Sylvain Gelly (7) Nino Vieillard (6) Olivier Pietquin (6)

Keywords

disentangled representation (6) representation learning (4) unsupervised learning (4) inductive bia (3) reinforcement learning (3) imitation learning (3) k-means clustering (3) policy learning (3) provable guarantee (2) self-supervised learning (2) generative adversarial network (2) abstract reasoning (2) data summarization (2) continuous control (2) reward function (2) variational autoencoder (2) policy gradient (2) transfer learning (1) approximate inference (1) image generation (1)

Papers

BOND: Aligning LLMs with Best-of-N Distillation ICLR 2025 Diversity-Rewarded CFG Distillation ICLR 2025 MusicRL: Aligning Music Generation to Human Preferences ICML 2024 On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes ICLR 2024 Imitating Language via Scalable Inverse Reinforcement Learning NIPS 2024 WARM: On the Benefits of Weight Averaged Reward Models ICML 2024 Nash Learning from Human Feedback ICML 2024 Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning EMNLP 2024 Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback ACL 2023 The Role of Pretrained Representations for the OOD Generalization of RL Agents ICLR 2022 Offline Reinforcement Learning as Anti-exploration AAAI 2022 Decoding a Neural Retriever’s Latent Space for Query Suggestion EMNLP 2022 A general class of surrogate functions for stable and efficient reinforcement learning AISTATS 2022 What Matters for Adversarial Imitation Learning? NIPS 2021 What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study ICLR 2021 Hyperparameter Selection for Imitation Learning ICML 2021 A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation JMLR 2020 Google Research Football: A Novel Reinforcement Learning Environment AAAI 2020 Precision-Recall Curves Using Information Divergence Frontiers AISTATS 2020 Disentangling Factors of Variations Using Few Labels ICLR 2020 Weakly-Supervised Disentanglement Without Compromises ICML 2020 Automatic Shortcut Removal for Self-Supervised Representation Learning ICML 2020 On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset NIPS 2019 On the Fairness of Disentangled Representations NIPS 2019 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations ICML 2019 High-Fidelity Image Generation With Fewer Labels ICML 2019 Are Disentangled Representations Helpful for Abstract Visual Reasoning? NIPS 2019 Assessing Generative Models via Precision and Recall NIPS 2018 One-shot Coresets: The Case of k-Clustering AISTATS 2018 Distributed and Provably Good Seedings for k-Means in Constant Rounds ICML 2017 Uniform Deviation Bounds for k-Means Clustering ICML 2017 Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures AISTATS 2016 Horizontally Scalable Submodular Maximization ICML 2016 Linear-Time Outlier Detection via Sensitivity IJCAI 2016 Fast and Provably Good Seedings for k-Means NIPS 2016 Coresets for Nonparametric Estimation - the Case of DP-Means ICML 2015