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cs.LG2026
Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions
Willem Diepeveen, Deanna Needell
Classical archetypal analysis is appealing for its interpretability, but its linear geometry can limit performance on data with strongly non-linear structure; at the same time, exi…
stat.ML2026
Harmful Overfitting in Sobolev Spaces
Kedar Karhadkar, Alexander Sietsema, Deanna Needell +1
Motivated by recent work on benign overfitting in overparameterized machine learning, we study the generalization behavior of functions in Sobolev spaces t…
cs.LG2026
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
Xue Feng, Li Wang, Deanna Needell +1
The Jordan-Kinderlehrer-Otto (JKO) scheme provides a stable variational framework for computing Wasserstein gradient flows, but its practical use is often limited by the high compu…