Tianbao Yang

Variance-Reduced Off-Policy Memory-Efficient Policy Search

2020 · arXiv (Cornell University)

Off-policy policy optimization is a challenging problem in reinforcement learning (RL). The algorithms designed for this problem often suffer from high variance in their estimators, which results in poor sample efficiency, and have issues with …

Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices

2022 · arXiv (Cornell University)

The area under the ROC curve (AUROC) has been vigorously applied for imbalanced classification and moreover combined with deep learning techniques. However, there is no existing work that provides sound information for peers to choose …

Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition

2020 · arXiv (Cornell University)

This paper focuses on stochastic methods for solving smooth non-convex strongly-concave min-max problems, which have received increasing attention due to their potential applications in deep learning (e.g., deep AUC maximization, distributionally robust optimization). However, most …