538 citations
- University College LondonGB83 papers
- University of CambridgeGB12 papers
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- Institut national de recherche en sciences et technologies du numériqueFR8 papers
- Max Planck Institute for Intelligent SystemsDE7 papers
- University of LondonGB7 papers
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- The Institute of Statistical MathematicsJP6 papers
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34 papers · 1 filter
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…
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…
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…
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…