Olivier Bachem

Affiliations:
  • ETH Zurich, Department of Computer Science, Switzerland


According to our database1, Olivier Bachem authored at least 41 papers between 2015 and 2021.

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Bibliography

2021
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization.
CoRR, 2021

A functional mirror ascent view of policy gradient methods with function approximation.
CoRR, 2021

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning.
CoRR, 2021

Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation.
CoRR, 2021

Offline Reinforcement Learning as Anti-Exploration.
CoRR, 2021

Concave Utility Reinforcement Learning: the Mean-field Game viewpoint.
CoRR, 2021

What Matters for Adversarial Imitation Learning?
CoRR, 2021

Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets.
CoRR, 2021

Hyperparameter Selection for Imitation Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation.
J. Mach. Learn. Res., 2020

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study.
CoRR, 2020

Automatic Shortcut Removal for Self-Supervised Representation Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Weakly-Supervised Disentanglement Without Compromises.
Proceedings of the 37th International Conference on Machine Learning, 2020

Disentangling Factors of Variations Using Few Labels.
Proceedings of the 8th International Conference on Learning Representations, 2020

Precision-Recall Curves Using Information Divergence Frontiers.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A Commentary on the Unsupervised Learning of Disentangled Representations.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Google Research Football: A Novel Reinforcement Learning Environment.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
The Visual Task Adaptation Benchmark.
CoRR, 2019

Evaluating Generative Models Using Divergence Frontiers.
CoRR, 2019

Disentangling Factors of Variation Using Few Labels.
CoRR, 2019

Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Fairness of Disentangled Representations.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

High-Fidelity Image Generation With Fewer Labels.
Proceedings of the 36th International Conference on Machine Learning, 2019

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.
Proceedings of the Reproducibility in Machine Learning, 2019

2018
Recent Advances in Autoencoder-Based Representation Learning.
CoRR, 2018

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.
CoRR, 2018

Assessing Generative Models via Precision and Recall.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scalable k -Means Clustering via Lightweight Coresets.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

One-shot Coresets: The Case of k-Clustering.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means.
CoRR, 2017

Scalable and Distributed Clustering via Lightweight Coresets.
CoRR, 2017

Uniform Deviation Bounds for k-Means Clustering.
Proceedings of the 34th International Conference on Machine Learning, 2017

Distributed and Provably Good Seedings for k-Means in Constant Rounds.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Fast and Provably Good Seedings for k-Means.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Linear-Time Outlier Detection via Sensitivity.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Horizontally Scalable Submodular Maximization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Approximate K-Means++ in Sublinear Time.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Coresets for Nonparametric Estimation - the Case of DP-Means.
Proceedings of the 32nd International Conference on Machine Learning, 2015


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