805 citations · 1.9k across the 55 of their papers we have counts for
7 papers · 1 filter
MAUVE Scores for Generative Models: Theory and Practice
Krishna Pillutla, Lang Liu, John Thickstun +6
Generative artificial intelligence has made significant strides, producing text indistinguishable from human prose and remarkably photorealistic images. Automatically measuring how…
Target Propagation via Regularized Inversion
Vincent Roulet, Zaid Harchaoui
Target Propagation (TP) algorithms compute targets instead of gradients along neural networks and propagate them backward in a way that is similar yet different than gradient back-…
Federated Learning with Superquantile Aggregation for Heterogeneous Data
Krishna Pillutla, Yassine Laguel, Jérôme Malick +1
We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach…
Faster Policy Learning with Continuous-Time Gradients
Samuel Ainsworth, Kendall Lowrey, John Thickstun +2
We study the estimation of policy gradients for continuous-time systems with known dynamics. By reframing policy learning in continuous-time, we show that it is possible construct…
An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks
Vincent Roulet, Zaid Harchaoui
We present an approach to obtain convergence guarantees of optimization algorithms for deep networks based on elementary arguments and computations. The convergence analysis revolv…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Fast and Simple Optimization for Poisson Likelihood Models
Niao He, Zaid Harchaoui, Yichen Wang +1
Poisson likelihood models have been prevalently used in imaging, social networks, and time series analysis. We propose fast, simple, theoretically-grounded, and versatile, optimiza…