3 papers
stat.ML2025
Data Depth as a Risk
Arturo Castellanos, Pavlo Mozharovskyi
Data depths are score functions that quantify in an unsupervised fashion how central is a point inside a distribution, with numerous applications such as anomaly detection, multiva…
stat.ML2025
Kernel Trace Distance: Quantum Statistical Metric between Measures through RKHS Density Operators
Arturo Castellanos, Anna Korba, Pavlo Mozharovskyi +1
Distances between probability distributions are a key component of many statistical machine learning tasks, from two-sample testing to generative modeling, among others. We introdu…
stat.ML2023
Fast kernel half-space depth for data with non-convex supports
Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc +1
Data depth is a statistical function that generalizes order and quantiles to the multivariate setting and beyond, with applications spanning over descriptive and visual statistics,…