287 citations · 1.1k across the 60 of their papers we have counts for
70 papers
Slicing Mutual Information Generalization Bounds for Neural Networks
Kimia Nadjahi, Kristjan Greenewald, Rickard Brüel Gabrielsson +1
The ability of machine learning (ML) algorithms to generalize well to unseen data has been studied through the lens of information theory, by bounding the generalization error with…
A Convex Optimization Framework for Regularized Geodesic Distances
Michal Edelstein, Nestor Guillen, Justin Solomon +1
We propose a general convex optimization problem for computing regularized geodesic distances. We show that under mild conditions on the regularizer the problem is well posed. We p…
Data-Free Learning of Reduced-Order Kinematics
Nicholas Sharp, Cristian Romero, Alec Jacobson +4
Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrate…
Self-Consistent Velocity Matching of Probability Flows
Lingxiao Li, Samuel Hurault, Justin Solomon
We present a discretization-free scalable framework for solving a large class of mass-conserving partial differential equations (PDEs), including the time-dependent Fokker-Planck e…
Outlier-Robust Group Inference via Gradient Space Clustering
Yuchen Zeng, Kristjan Greenewald, Kangwook Lee +2
Traditional machine learning models focus on achieving good performance on the overall training distribution, but they often underperform on minority groups. Existing methods can i…
Sampling with Mollified Interaction Energy Descent
Lingxiao Li, Qiang Liu, Anna Korba +2
Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper,…