Olivier Bachem

发表

On the Fairness of Disentangled Representations

Stefan Bauer, Bernhard Schölkopf, Francesco Locatello, 2019, NeurIPS.

One-Shot Coresets: The Case of k-Clustering pdf

Silvio Lattanzi, Olivier Bachem, Mario Lucic, 2017, AISTATS.

On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset

Bernhard Schölkopf, Francesco Locatello, Olivier Bachem, 2019, NeurIPS 2019.

Approximate K-Means++ in Sublinear Time

Andreas Krause, Olivier Bachem, Mario Lucic, 2016, AAAI.

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Xiaohua Zhai, Lucas Beyer, Maxim Neumann, 2019 .

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation pdf

Francesco Locatello, Olivier Bachem, Mario Lucic, 2020, J. Mach. Learn. Res..

Scalable k -Means Clustering via Lightweight Coresets

Andreas Krause, Olivier Bachem, Mario Lucic, 2017, KDD.

Practical Coreset Constructions for Machine Learning

Andreas Krause, Olivier Bachem, Mario Lucic, 2017, 1703.06476.

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study pdf

Marcin Andrychowicz, Matthieu Geist, Olivier Pietquin, 2020, ArXiv.

Google Research Football: A Novel Reinforcement Learning Environment pdf

Olivier Bachem, Olivier Bousquet, Sylvain Gelly, 2020, AAAI.

Scalable and Distributed Clustering via Lightweight Coresets pdf

Andreas Krause, Olivier Bachem, Mario Lucic, 2017, ArXiv.

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Stefan Bauer, Bernhard Schölkopf, Francesco Locatello, 2019, NeurIPS.

Sampling for Large-Scale Clustering

Olivier Bachem, Olivier Bachem, 2018 .

Uniform Deviation Bounds for k-Means Clustering

Andreas Krause, Olivier Bachem, Mario Lucic, 2017, ICML.

Weakly-Supervised Disentanglement Without Compromises pdf

Francesco Locatello, Olivier Bachem, Michael Tschannen, 2020, ICML.

Linear-Time Outlier Detection via Sensitivity

Andreas Krause, Olivier Bachem, Mario Lucic, 2016, IJCAI.

Recent Advances in Autoencoder-Based Representation Learning pdf

Olivier Bachem, Michael Tschannen, Mario Lucic, 2018, ArXiv.

Distributed and Provably Good Seedings for k-Means in Constant Rounds

Andreas Krause, Olivier Bachem, Mario Lucic, 2017, ICML.

Disentangling Factors of Variations Using Few Labels pdf

Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, 2020, ICLR.

Automatic Shortcut Removal for Self-Supervised Representation Learning

Olivier Bachem, Michael Tschannen, Neil Houlsby, 2020, ICML.

Evaluating Generative Models Using Divergence Frontiers pdf

Olivier Bachem, Marco Cuturi, Mario Lucic, 2019, ArXiv.

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures

Andreas Krause, Olivier Bachem, Mario Lucic, 2015, AISTATS.

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

Sjoerd van Steenkiste, Jurgen Schmidhuber, Francesco Locatello, 2019, NeurIPS.

Coresets for Nonparametric Estimation - the Case of DP-Means

Andreas Krause, Olivier Bachem, Mario Lucic, 2015, ICML.

A Commentary on the Unsupervised Learning of Disentangled Representations pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2020, AAAI.

Precision-Recall Curves Using Information Divergence Frontiers

Olivier Bachem, Marco Cuturi, Mario Lucic, 2019, AISTATS.

Assessing Generative Models via Precision and Recall

Olivier Bachem, Mario Lucic, Sylvain Gelly, 2018, NeurIPS.

The Visual Task Adaptation Benchmark pdf

Xiaohua Zhai, Lucas Beyer, Alexander Kolesnikov, 2019, ArXiv.

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means pdf

Andreas Krause, Olivier Bachem, Mario Lucic, 2017, ICML 2017.

Horizontally Scalable Submodular Maximization pdf

Morteza Zadimoghaddam, Andreas Krause, Olivier Bachem, 2016, ICML.

High-Fidelity Image Generation With Fewer Labels pdf

Xiaohua Zhai, Olivier Bachem, Michael Tschannen, 2019, ICML.

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations pdf

Bernhard Schölkopf, Francesco Locatello, Olivier Bachem, 2018, ICML.

Fast and Provably Good Seedings for k-Means

Andreas Krause, Olivier Bachem, Mario Lucic, 2016, NIPS.

Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation pdf

Olivier Bachem, Sertan Girgin, Igor Mordatch, 2021, NeurIPS Datasets and Benchmarks.

Hyperparameter Selection for Imitation Learning

Marcin Andrychowicz, Matthieu Geist, Olivier Pietquin, 2021, ICML.

Concave Utility Reinforcement Learning: the Mean-field Game viewpoint pdf

Matthieu Geist, Olivier Pietquin, Olivier Bachem, 2021, ArXiv.

Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets pdf

David Simcha, Olivier Bachem, Pradipta Mitra, 2021, ArXiv.

Offline Reinforcement Learning as Anti-Exploration pdf

Matthieu Geist, Olivier Pietquin, Olivier Bachem, 2021, AAAI.

The Role of Pretrained Representations for the OOD Generalization of RL Agents

Ole Winther, Olivier Bachem, Bernhard Scholkopf, 2021 .

A general class of surrogate functions for stable and efficient reinforcement learning

Nicolas Le Roux, Marlos C. Machado, Matthieu Geist, 2021 .

Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization pdf

Olivier Bachem, Seyed Kamyar Seyed Ghasemipour, Erwin Coumans, 2021, ArXiv.

What Matters for Adversarial Imitation Learning? pdf

Marcin Andrychowicz, Matthieu Geist, Olivier Pietquin, 2021, NeurIPS.

A functional mirror ascent view of policy gradient methods with function approximation pdf

Nicolas Le Roux, Marlos C. Machado, Matthieu Geist, 2021, ArXiv.

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning pdf

Ole Winther, Peter V. Gehler, Bernhard Schölkopf, 2021, ArXiv.