519 citations · 1.6k across the 49 of their papers we have counts for
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Uniform Convergence of Gradients for Non-Convex Learning and Optimization
Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
We investigate 1) the rate at which refined properties of the empirical risk---in particular, gradients---converge to their population counterparts in standard non-convex learning…
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
Andrew Cotter, Heinrich Jiang, Serena Wang +4
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study th…
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang +5
Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the gene…
Two-Player Games for Efficient Non-Convex Constrained Optimization
Andrew Cotter, Heinrich Jiang, Karthik Sridharan
In recent years, constrained optimization has become increasingly relevant to the machine learning community, with applications including Neyman-Pearson classification, robust opti…
Online Learning: Sufficient Statistics and the Burkholder Method
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
We uncover a fairly general principle in online learning: If regret can be (approximately) expressed as a function of certain "sufficient statistics" for the data sequence, then th…
Logistic Regression: The Importance of Being Improper
Dylan J. Foster, Satyen Kale, Haipeng Luo +2
Learning linear predictors with the logistic loss---both in stochastic and online settings---is a fundamental task in machine learning and statistics, with direct connections to cl…