358 citations · 721 across the 6 of their papers we have counts for
6 papers
A Commentary on the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In…
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk +9
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple,…
Automatic Shortcut Removal for Self-Supervised Representation Learning
Matthias Minderer, Olivier Bachem, Neil Houlsby +1
In self-supervised visual representation learning, a feature extractor is trained on a "pretext task" for which labels can be generated cheaply, without human annotation. A central…
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means
Olivier Bachem, Mario Lucic, S. Hamed Hassani +1
Uniform deviation bounds limit the difference between a model's expected loss and its loss on an empirical sample uniformly for all models in a learning problem. As such, they are…
Horizontally Scalable Submodular Maximization
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam +1
A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization. Existing approaches for distributed submodular maximization have…
Linear-time Outlier Detection via Sensitivity
Mario Lucic, Olivier Bachem, Andreas Krause
Outliers are ubiquitous in modern data sets. Distance-based techniques are a popular non-parametric approach to outlier detection as they require no prior assumptions on the data g…