11 citations · 24 across the 11 of their papers we have counts for
12 papers
DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
Zhijian Zhou, Xunye Tian, Liuhua Peng +4
To adapt kernel two-sample and independence testing to complex structured data, aggregation of multiple kernels is frequently employed to boost testing power compared to single-ker…
A Unified View of Optimal Kernel Hypothesis Testing
Antonin Schrab
This paper provides a unifying view of optimal kernel hypothesis testing across the MMD two-sample, HSIC independence, and KSD goodness-of-fit frameworks. Minimax optimal separatio…
A Practical Introduction to Kernel Discrepancies: MMD, HSIC & KSD
Antonin Schrab
This article provides a practical introduction to kernel discrepancies, focusing on the Maximum Mean Discrepancy (MMD), the Hilbert-Schmidt Independence Criterion (HSIC), and the K…
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…
Robust Kernel Hypothesis Testing under Data Corruption
Antonin Schrab, Ilmun Kim
We propose a general method for constructing robust permutation tests under data corruption. The proposed tests effectively control the non-asymptotic type I error under data corru…
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…
Differentially Private Permutation Tests
Ilmun Kim, Antonin Schrab
Recent years have witnessed growing concerns about the privacy of sensitive data. In response to these concerns, differential privacy has emerged as a rigorous framework for privac…
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
Felix Biggs, Antonin Schrab, Arthur Gretton
We propose novel statistics which maximise the power of a two-sample test based on the Maximum Mean Discrepancy (MMD), by adapting over the set of kernels used in defining it. For…
Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'
Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó +2
We discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based…
Efficient Aggregated Kernel Tests using Incomplete -statistics
Antonin Schrab, Ilmun Kim, Benjamin Guedj +1
We propose a series of computationally efficient nonparametric tests for the two-sample, independence, and goodness-of-fit problems, using the Maximum Mean Discrepancy (MMD), Hilbe…
KSD Aggregated Goodness-of-fit Test
Antonin Schrab, Benjamin Guedj, Arthur Gretton
We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAgg, which aggregates multipl…
MMD Aggregated Two-Sample Test
Antonin Schrab, Ilmun Kim, Mélisande Albert +3
We propose two novel nonparametric two-sample kernel tests based on the Maximum Mean Discrepancy (MMD). First, for a fixed kernel, we construct an MMD test using either permutation…