602 citations · 2.9k across the 103 of their papers we have counts for
105 papers
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…
Credal Two-Sample Tests of Epistemic Uncertainty
Siu Lun Chau, Antonin Schrab, Arthur Gretton +2
We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric un…
(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…
Practical Kernel Tests of Conditional Independence
Roman Pogodin, Antonin Schrab, Yazhe Li +2
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the corre…
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…
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…