274 citations · 423 across the 15 of their papers we have counts for
4 papers · 1 filter
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Julius von Kügelgen, Paul K Rubenstein, Bernhard Schölkopf +1
We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform thos…
On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein +2
Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different…
Practical and Consistent Estimation of f-Divergences
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga +2
The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning. Most works study this problem…
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou +2
We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple…