7 papers · 1 filter
(De)-regularized Maximum Mean Discrepancy Gradient Flow
Zonghao Chen, Aratrika Mustafi, Pierre Glaser +3
We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows that transport samples from source distributio…
Fast and Scalable Score-Based Kernel Calibration Tests
Pierre Glaser, David Widmann, Fredrik Lindsten +1
We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a non-parametric, kernel-based test for assessing the calibration of probabilistic models with…
Controlling Moments with Kernel Stein Discrepancies
Heishiro Kanagawa, Alessandro Barp, Arthur Gretton +1
Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. No…
Nonlinear Meta-Learning Can Guarantee Faster Rates
Dimitri Meunier, Zhu Li, Arthur Gretton +1
Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target ta…
Composite Goodness-of-fit Tests with Kernels
Oscar Key, Arthur Gretton, François-Xavier Briol +1
Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly…
Kernel Single Proxy Control for Deterministic Confounding
Liyuan Xu, Arthur Gretton
We consider the problem of causal effect estimation with an unobserved confounder, where we observe a single proxy variable that is associated with the confounder. Although it has…