Multivariate (generalized) scattered data interpolation with symmetric (conditionally) positive definite kernel functions in arbitrary dimension
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Updated
Jun 20, 2024 - Julia
Multivariate (generalized) scattered data interpolation with symmetric (conditionally) positive definite kernel functions in arbitrary dimension
SPLASH is an interactive visualisation and plotting tool using kernel interpolation, mainly used for Smoothed Particle Hydrodynamics simulations
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Hardware-based attestation / intrusion detection app for Android devices. It provides both local verification with another Android device via QR codes and optional scheduled server-based verification with support for alert emails. It uses hardware-backed keys and attestation support as the foundation and chains trust to the app for software checks.
Exploring "variability collapse" in shallow neural networks
Code for reimplementing "Stein Variational Gradient Descent (SVGD)" method.
Reproducibility code for Robust Kernel Hypothesis Testing under Data Corruption, by Schrab and Kim
dckernel package implementing dcMMD and dcHSIC from Robust Kernel Hypothesis Testing under Data Corruption, by Schrab and Kim
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
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
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
Reproducibility code for MMD Aggregated Two-Sample Test, by Schrab, Kim, Albert, Laurent, Guedj and Gretton: https://arxiv.org/abs/2110.15073
Reproducibility code for Differentially Private Permutation Tests: Applications to Kernel Methods, by Kim and Schrab
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
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
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
Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test. https://arxiv.org/abs/2202.00824 NeurIPS 2022
dpkernel package implementing dpMMD and dpHSIC from Differentially Private Permutation Tests: Applications to Kernel Methods, by Kim and Schrab
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