5 papers
Sharper Analysis of Single-Loop Methods for Bilevel Optimization
Yubo Zhou, Jun Shu, Luo Luo +4
Bilevel optimization underpins many machine learning applications, including hyperparameter optimization, meta-learning, neural architecture search, and reinforcement learning. Whi…
Why Does Adaptive Zeroth-Order Optimization Work?
Haishan Ye, Luo Luo
Zeroth-order (ZO) optimization is popular in real-world applications that accessing the gradient information is expensive or unavailable. Recently, adaptive ZO methods that normali…
On the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation
Yubo Zhou, Luo Luo, Guang Dai +1
Stochastic Bilevel Optimization has emerged as a fundamental framework for meta-learning and hyperparameter optimization. Despite the practical prevalence of single-loop algorithms…
Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
Jing Xue, Zhishen Sun, Haishan Ye +4
Membership inference attack (MIA) has become one of the most widely used and effective methods for evaluating the privacy risks of machine learning models. These attacks aim to det…
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity
Qihao Zhou, Haishan Ye, Luo Luo
This paper considers the distributed convex-concave minimax optimization under the second-order similarity. We propose stochastic variance-reduced optimistic gradient sliding (SVOG…