3 papers
stat.ML2026
Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD
Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán Szabó
Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications…
stat.ML2025
The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
Jose Cribeiro-Ramallo, Agnideep Aich, Florian Kalinke +2
Kernel Stein discrepancies (KSDs) have emerged as a powerful tool for quantifying goodness-of-fit over the last decade, featuring numerous successful applications. To the best of o…
cs.LG2025
Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data
Jose Cribeiro-Ramallo, Federico Matteucci, Paul Enciu +4
Outlier detection in high-dimensional tabular data is challenging since data is often distributed across multiple lower-dimensional subspaces -- a phenomenon known as the Multiple…