811 citations · 917 across the 8 of their papers we have counts for
8 papers
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…
Stochastic Optimization under Distributional Drift
Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui
We consider the problem of minimizing a convex function that is evolving according to unknown and possibly stochastic dynamics, which may depend jointly on time and on the decision…
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…
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers +4
As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comp…
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…
Learning to track for spatio-temporal action localization
Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid
We propose an effective approach for spatio-temporal action localization in realistic videos. The approach first detects proposals at the frame-level and scores them with a combina…