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agginc

AggInc package implementing the MMDAggInc, HSICAggInc and KSDAggInc tests proposed in Efficient Aggregated Kernel Tests using Incomplete U-statistics by Schrab, Kim, Guedj and Gretton: https://arxiv.org/abs/2206.09194 NeurIPS 2022

agginc-paper

Reproducibility code for Efficient Aggregated Kernel Tests using Incomplete U-statistics, by Schrab, Kim, Guedj and Gretton: https://arxiv.org/abs/2206.09194 NeurIPS 2022

antoninschrab.github.io

Forked from academicpages.github.io

datacorruption

dckernel

dckernel package implementing dcMMD and dcHSIC from Robust Kernel Hypothesis Testing under Data Corruption, by Schrab and Kim

dckernel-paper

Reproducibility code for Robust Kernel Hypothesis Testing under Data Corruption, by Schrab and Kim

dpkernel

dpkernel package implementing dpMMD and dpHSIC from Differentially Private Permutation Tests: Applications to Kernel Methods, by Kim and Schrab

dpkernel-paper

Reproducibility code for Differentially Private Permutation Tests: Applications to Kernel Methods, by Kim and Schrab

fl-mmdagg

ksdagg

KSDAgg package implementing the KSDAgg test proposed in KSD Aggregated Goodness-of-fit Test by Schrab, Guedj and Gretton: https://arxiv.org/abs/2202.00824 NeurIPS 2022

ksdagg-paper

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test. https://arxiv.org/abs/2202.00824 NeurIPS 2022

mmdagg

MMDAgg package implementing the MMDAgg test proposed in MMD Aggregated Two-Sample Test by Schrab, Kim, Albert, Laurent, Guedj and Gretton: https://arxiv.org/abs/2110.15073

mmdagg-paper

Reproducibility code for MMD Aggregated Two-Sample Test, by Schrab, Kim, Albert, Laurent, Guedj and Gretton: https://arxiv.org/abs/2110.15073

mmdfuse

MMD-FUSE package implementing the MMD-FUSE test proposed in MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting by Biggs, Schrab, and Gretton: https://arxiv.org/abs/2306.08777

mmdfuse-paper

Reproducibility code for MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting, by Biggs, Schrab, and Gretton: https://arxiv.org/abs/2306.08777