6 papers
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…
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…
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…
Observational Multiplicity
Erin George, Deanna Needell, Berk Ustun
Many prediction tasks can admit multiple models that can perform almost equally well. This phenomenon can undermine interpretability and safety when competing models assign conflic…
Benign overfitting in leaky ReLU networks with moderate input dimension
Kedar Karhadkar, Erin George, Michael Murray +2
The problem of benign overfitting asks whether it is possible for a model to perfectly fit noisy training data and still generalize well. We study benign overfitting in two-layer l…
Super-resolution via superset selection and pruning
Laurent Demanet, Deanna Needell, Nam Nguyen
We present a pursuit-like algorithm that we call the "superset method" for recovery of sparse vectors from consecutive Fourier measurements in the super-resolution regime. The algo…