171 citations · 357 across the 5 of their papers we have counts for
5 papers
Unbiased estimators for the variance of MMD estimators
Danica J. Sutherland, Namrata Deka
The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with p…
Demystifying MMD GANs
Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel +1
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribu…
Bayesian Approaches to Distribution Regression
Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic +1
Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than…
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann +4
We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test bas…
Deep Mean Maps
Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos +1
The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision. Both of these methodologies have separately achieved a gre…