3 citations · 3 across the 8 of their papers we have counts for
7 papers · 1 filter
Measuring Differences between Conditional Distributions using Kernel Embeddings
Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic
Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for meas…
Instrumental and Proximal Causal Inference with Gaussian Processes
Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic +2
Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial m…
Kernel Quantile Embeddings and Associated Probability Metrics
Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol +1
Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as the maximum mean discrepancy (MMD), a statistic…
Integral Imprecise Probability Metrics
Siu Lun Chau, Michele Caprio, Krikamol Muandet
Quantifying differences between probability distributions is fundamental to statistics and machine learning, primarily for comparing statistical uncertainty. In contrast, epistemic…
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…
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs. Our method is based on the popular sol…