811 citations · 917 across the 8 of their papers we have counts for
5 papers · 1 filter
Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
Ronak Mehta, Jelena Diakonikolas, Zaid Harchaoui
We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using -DRO and spectral/$…
Entropy Regularized Optimal Transport Independence Criterion
Lang Liu, Soumik Pal, Zaid Harchaoui
We introduce an independence criterion based on entropy regularized optimal transport. Our criterion can be used to test for independence between two samples. We establish non-asym…
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
Lang Liu, Krishna Pillutla, Sean Welleck +3
The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an eval…
Robust Aggregation for Federated Learning
Krishna Pillutla, Sham M. Kakade, Zaid Harchaoui
Federated learning is the centralized training of statistical models from decentralized data on mobile devices while preserving the privacy of each device. We present a robust aggr…
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
We propose an inexact variable-metric proximal point algorithm to accelerate gradient-based optimization algorithms. The proposed scheme, called QNing can be notably applied to inc…