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


Oct 27, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* Journal of Machine Learning Research 2020, Volume 21, Number 209 
* arXiv admin note: substantial text overlap with arXiv:1811.12359 

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A Commentary on the Unsupervised Learning of Disentangled Representations


Jul 28, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 (AAAI-20) 

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


Jun 10, 2020
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem


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Automatic Shortcut Removal for Self-Supervised Representation Learning


Feb 21, 2020
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen


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Weakly-Supervised Disentanglement Without Compromises


Feb 07, 2020
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen


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The Visual Task Adaptation Benchmark


Oct 01, 2019
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby


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Google Research Football: A Novel Reinforcement Learning Environment


Jul 25, 2019
Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly


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


Jun 07, 2019
Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer


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On the Fairness of Disentangled Representations


May 31, 2019
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem


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Are Disentangled Representations Helpful for Abstract Visual Reasoning?


May 29, 2019
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem

* This is a preliminary pre-print 

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Evaluating Generative Models Using Divergence Frontiers


May 26, 2019
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly


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Disentangling Factors of Variation Using Few Labels


May 03, 2019
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem


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High-Fidelity Image Generation With Fewer Labels


Mar 06, 2019
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly

* Mario Lucic, Michael Tschannen, and Marvin Ritter contributed equally to this work 

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Recent Advances in Autoencoder-Based Representation Learning


Dec 12, 2018
Michael Tschannen, Olivier Bachem, Mario Lucic

* Presented at the third workshop on Bayesian Deep Learning (NeurIPS 2018) 

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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations


Dec 02, 2018
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* This is a preliminary preprint based on our initial experimental results 

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Assessing Generative Models via Precision and Recall


Oct 28, 2018
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

* NIPS 2018 

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Scalable k-Means Clustering via Lightweight Coresets


Jun 06, 2018
Olivier Bachem, Mario Lucic, Andreas Krause

* To appear in the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 

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One-Shot Coresets: The Case of k-Clustering


Feb 20, 2018
Olivier Bachem, Mario Lucic, Silvio Lattanzi

* To Appear In AISTATS 2018 

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Practical Coreset Constructions for Machine Learning


Jun 04, 2017
Olivier Bachem, Mario Lucic, Andreas Krause


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Uniform Deviation Bounds for Unbounded Loss Functions like k-Means


Feb 27, 2017
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause


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Horizontally Scalable Submodular Maximization


May 31, 2016
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause


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Linear-time Outlier Detection via Sensitivity


May 02, 2016
Mario Lucic, Olivier Bachem, Andreas Krause

* 8 pages 

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


May 02, 2016
Mario Lucic, Olivier Bachem, Andreas Krause

* 14 pages, camera ready version 

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