Olivier Bachem

87 papers A* 31A 5Journal 46Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	BOND: Aligning LLMs with Best-of-N Distillation. ICLR Pier Giuseppe Sessa, Robert Dadashi-Tazehozi, Léonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Ramé, 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
2025	A*	`conf`	Diversity-Rewarded CFG Distillation. ICLR Geoffrey Cideron, Andrea Agostinelli, Johan Ferret, Sertan Girgin, Romuald Elie, Olivier Bachem, Sarah Perrin, Alexandre Ramé
2024	J	`jnl`	BOND: Aligning LLMs with Best-of-N Distillation. CoRR 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
2024	—	`conf`	Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. EMNLP (Findings) 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
2024	J	`jnl`	Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. CoRR 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
2024	J	`jnl`	Diversity-Rewarded CFG Distillation. CoRR Geoffrey Cideron, Andrea Agostinelli, Johan Ferret, Sertan Girgin, Romuald Elie, Olivier Bachem, Sarah Perrin, Alexandre Ramé
2024	A*	`conf`	Imitating Language via Scalable Inverse Reinforcement Learning. NeurIPS Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin A. Riedmiller
2024	J	`jnl`	Imitating Language via Scalable Inverse Reinforcement Learning. CoRR Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg 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
2024	A*	`conf`	MusicRL: Aligning Music Generation to Human Preferences. ICML 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
2024	J	`jnl`	MusicRL: Aligning Music Generation to Human Preferences. CoRR 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
2024	A*	`conf`	Nash Learning from Human Feedback. ICML Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, 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
2024	A*	`conf`	On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem
2024	J	`jnl`	RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR 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
2024	A*	`conf`	WARM: On the Benefits of Weight Averaged Reward Models. ICML Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
2024	J	`jnl`	WARM: On the Benefits of Weight Averaged Reward Models. CoRR Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
2024	J	`jnl`	WARP: On the Benefits of Weight Averaged Rewarded Policies. CoRR Alexandre Ramé, Johan Ferret, Nino Vieillard, Robert Dadashi, Léonard Hussenot, Pierre-Louis Cedoz, Pier Giuseppe Sessa, Sertan Girgin, Arthur Douillard, Olivier Bachem
2023	—	`conf`	Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. ACL (1) 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
2023	J	`jnl`	Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. CoRR 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
2023	J	`jnl`	GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem
2023	J	`jnl`	Nash Learning from Human Feedback. CoRR 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
2022	A	`conf`	A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
2022	J	`jnl`	C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. CoRR Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem
2022	A	`conf`	Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. AAMAS Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin
2022	A*	`conf`	Decoding a Neural Retriever's Latent Space for Query Suggestion. EMNLP Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann
2022	J	`jnl`	Decoding a Neural Retriever's Latent Space for Query Suggestion. CoRR Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann
2022	A*	`conf`	Offline Reinforcement Learning as Anti-exploration. AAAI Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
2022	A*	`conf`	The Role of Pretrained Representations for the OOD Generalization of RL Agents. ICLR Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
2022	J	`jnl`	vec2text with Round-Trip Translations. CoRR Geoffrey Cideron, Sertan Girgin, Anton Raichuk, Olivier Pietquin, Olivier Bachem, Léonard Hussenot
2021	J	`jnl`	A functional mirror ascent view of policy gradient methods with function approximation. CoRR Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
2021	—	`conf`	Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. NeurIPS Datasets and Benchmarks C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem
2021	J	`jnl`	Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation. CoRR C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem
2021	J	`jnl`	Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization. CoRR Shixiang Shane Gu, Manfred Diaz, C. Daniel Freeman, Hiroki Furuta, Seyed Kamyar Seyed Ghasemipour, Anton Raichuk, Byron David, Erik Frey, Erwin Coumans, Olivier Bachem
2021	J	`jnl`	Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. CoRR Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin
2021	A*	`conf`	Hyperparameter Selection for Imitation Learning. ICML 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
2021	J	`jnl`	Hyperparameter Selection for Imitation Learning. CoRR 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
2021	J	`jnl`	Offline Reinforcement Learning as Anti-Exploration. CoRR Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
2021	J	`jnl`	Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. CoRR Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter V. Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
2021	J	`jnl`	Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets. CoRR Baris Sumengen, Anand Rajagopalan, Gui Citovsky, David Simcha, Olivier Bachem, Pradipta Mitra, Sam Blasiak, Mason Liang, Sanjiv Kumar
2021	A*	`conf`	What Matters for Adversarial Imitation Learning? NeurIPS Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz
2021	J	`jnl`	What Matters for Adversarial Imitation Learning? CoRR Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz
2021	A*	`conf`	What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. ICLR Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
2020	A*	`conf`	A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	J	`jnl`	A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2020	A*	`conf`	Automatic Shortcut Removal for Self-Supervised Representation Learning. ICML Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
2020	J	`jnl`	Automatic Shortcut Removal for Self-Supervised Representation Learning. CoRR Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
2020	A*	`conf`	Disentangling Factors of Variations Using Few Labels. ICLR Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
2020	A*	`conf`	Google Research Football: A Novel Reinforcement Learning Environment. AAAI Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly
2020	A	`conf`	Precision-Recall Curves Using Information Divergence Frontiers. AISTATS Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
2020	A*	`conf`	Weakly-Supervised Disentanglement Without Compromises. ICML Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
2020	J	`jnl`	Weakly-Supervised Disentanglement Without Compromises. CoRR Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen
2020	J	`jnl`	What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. CoRR Marcin Andrychowicz, Anton Raichuk, Piotr Stanczyk, Manu Orsini, Sertan Girgin, Raphaël Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem
2019	A*	`conf`	Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem
2019	J	`jnl`	Are Disentangled Representations Helpful for Abstract Visual Reasoning? CoRR Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem
2019	—	`conf`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2019	A*	`conf`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2019	J	`jnl`	Disentangling Factors of Variation Using Few Labels. CoRR Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem
2019	J	`jnl`	Evaluating Generative Models Using Divergence Frontiers. CoRR Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
2019	J	`jnl`	Google Research Football: A Novel Reinforcement Learning Environment. CoRR Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly
2019	A*	`conf`	High-Fidelity Image Generation With Fewer Labels. ICML Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
2019	J	`jnl`	High-Fidelity Image Generation With Fewer Labels. CoRR Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
2019	A*	`conf`	On the Fairness of Disentangled Representations. NeurIPS Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
2019	J	`jnl`	On the Fairness of Disentangled Representations. CoRR Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
2019	A*	`conf`	On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. NeurIPS Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
2019	J	`jnl`	On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. CoRR Muhammad Waleed Gondal, Manuel Wüthrich, Ðorðe Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
2019	J	`jnl`	The Visual Task Adaptation Benchmark. CoRR 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
2018	A*	`conf`	Assessing Generative Models via Precision and Recall. NeurIPS Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
2018	J	`jnl`	Assessing Generative Models via Precision and Recall. CoRR Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
2018	J	`jnl`	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
2018	A	`conf`	One-shot Coresets: The Case of k-Clustering. AISTATS Olivier Bachem, Mario Lucic, Silvio Lattanzi
2018	J	`jnl`	Recent Advances in Autoencoder-Based Representation Learning. CoRR Michael Tschannen, Olivier Bachem, Mario Lucic
2018	A*	`conf`	Scalable k -Means Clustering via Lightweight Coresets. KDD Olivier Bachem, Mario Lucic, Andreas Krause
2017	A*	`conf`	Distributed and Provably Good Seedings for k-Means in Constant Rounds. ICML Olivier Bachem, Mario Lucic, Andreas Krause
2017	J	`jnl`	One-Shot Coresets: The Case of k-Clustering. CoRR Olivier Bachem, Mario Lucic, Silvio Lattanzi
2017	J	`jnl`	Scalable and Distributed Clustering via Lightweight Coresets. CoRR Olivier Bachem, Mario Lucic, Andreas Krause
2017	J	`jnl`	Uniform Deviation Bounds for Unbounded Loss Functions like k-Means. CoRR Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
2017	A*	`conf`	Uniform Deviation Bounds for k-Means Clustering. ICML Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
2016	A*	`conf`	Approximate K-Means++ in Sublinear Time. AAAI Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
2016	—	`conf`	Fast and Provably Good Seedings for k-Means. NIPS Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause
2016	A*	`conf`	Horizontally Scalable Submodular Maximization. ICML Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
2016	J	`jnl`	Horizontally Scalable Submodular Maximization. CoRR Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
2016	A*	`conf`	Linear-Time Outlier Detection via Sensitivity. IJCAI Mario Lucic, Olivier Bachem, Andreas Krause
2016	J	`jnl`	Linear-time Outlier Detection via Sensitivity. CoRR Mario Lucic, Olivier Bachem, Andreas Krause
2016	A	`conf`	Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. AISTATS Mario Lucic, Olivier Bachem, Andreas Krause
2015	A*	`conf`	Coresets for Nonparametric Estimation - the Case of DP-Means. ICML Olivier Bachem, Mario Lucic, Andreas Krause
2015	J	`jnl`	Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. CoRR Mario Lucic, Olivier Bachem, Andreas Krause