Olivier Bachem

Seyed Kamyar Seyed Ghasemipour

Massimiliano Ciaramita

Thomas Hofmann

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint.

[BibT_eX]

[DOI]

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

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

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Offline Reinforcement Learning as Anti-exploration.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets.

[BibT_eX]

[DOI]

CoRR, 2021

What Matters for Adversarial Imitation Learning?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Hyperparameter Selection for Imitation Learning.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

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

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Automatic Shortcut Removal for Self-Supervised Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Weakly-Supervised Disentanglement Without Compromises.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Disentangling Factors of Variations Using Few Labels.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Precision-Recall Curves Using Information Divergence Frontiers.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A Commentary on the Unsupervised Learning of Disentangled Representations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Google Research Football: A Novel Reinforcement Learning Environment.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

The Visual Task Adaptation Benchmark.

[BibT_eX]

[DOI]

CoRR, 2019

Evaluating Generative Models Using Divergence Frontiers.

[BibT_eX]

[DOI]

CoRR, 2019

Disentangling Factors of Variation Using Few Labels.

[BibT_eX]

[DOI]

CoRR, 2019

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

[BibT_eX]

[DOI]

Sjoerd van Steenkiste

Francesco Locatello

Jürgen Schmidhuber

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Muhammad Waleed Gondal

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.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Recent Advances in Autoencoder-Based Representation Learning.

[BibT_eX]

[DOI]

Michael Tschannen

CoRR, 2018

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.

[BibT_eX]

[DOI]

CoRR, 2018

Assessing Generative Models via Precision and Recall.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

One-shot Coresets: The Case of k-Clustering.

[BibT_eX]

[DOI]

Silvio Lattanzi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

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

[BibT_eX]

[DOI]

CoRR, 2017

Scalable and Distributed Clustering via Lightweight Coresets.

[BibT_eX]

[DOI]

CoRR, 2017

Uniform Deviation Bounds for k-Means Clustering.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

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

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Fast and Provably Good Seedings for k-Means.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Horizontally Scalable Submodular Maximization.

[BibT_eX]

[DOI]

Morteza Zadimoghaddam

Proceedings of the 33nd International Conference on Machine Learning, 2016

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

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Approximate K-Means++ in Sublinear Time.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Coresets for Nonparametric Estimation - the Case of DP-Means.

[BibT_eX]

[DOI]