3 papers
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…
cs.LG2025
Observational Multiplicity
Erin George, Deanna Needell, Berk Ustun
Many prediction tasks can admit multiple models that can perform almost equally well. This phenomenon can can undermine interpretability and safety when competing models assign con…