38 citations · 78 across the 11 of their papers we have counts for
11 papers
Uniformly Stable Algorithms for Adversarial Training and Beyond
Jiancong Xiao, Jiawei Zhang, Zhi-Quan Luo +1
In adversarial machine learning, neural networks suffer from a significant issue known as robust overfitting, where the robust test accuracy decreases over epochs (Rice et al., 202…
Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise
Zijian Liu, Jiawei Zhang, Zhengyuan Zhou
We consider the stochastic optimization problem with smooth but not necessarily convex objectives in the heavy-tailed noise regime, where the stochastic gradient's noise is assumed…
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints
Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang +1
Offline reinforcement learning (RL) aims to find an optimal policy for Markov decision processes (MDPs) using a pre-collected dataset. In this work, we revisit the linear programmi…
On the Iteration Complexity of Smoothed Proximal ALM for Nonconvex Optimization Problem with Convex Constraints
Jiawei Zhang, Wenqiang Pu, Zhi-Quan Luo
It is well-known that the lower bound of iteration complexity for solving nonconvex unconstrained optimization problems is , which can be achieved by standard gradient de…
What is a Good Metric to Study Generalization of Minimax Learners?
Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang +1
Minimax optimization has served as the backbone of many machine learning (ML) problems. Although the convergence behavior of optimization algorithms has been extensively studied in…
Decentralized Non-Convex Learning with Linearly Coupled Constraints
Jiawei Zhang, Songyang Ge, Tsung-Hui Chang +1
Motivated by the need for decentralized learning, this paper aims at designing a distributed algorithm for solving nonconvex problems with general linear constraints over a multi-a…
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems
Zhiguo Wang, Jiawei Zhang, Tsung-Hui Chang +2
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems…
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
Jiawei Zhang, Peijun Xiao, Ruoyu Sun +1
Nonconvex-concave min-max problem arises in many machine learning applications including minimizing a pointwise maximum of a set of nonconvex functions and robust adversarial train…
A global dual error bound and its application to the analysis of linearly constrained nonconvex optimization
Jiawei Zhang, Zhiquan Luo
Error bound analysis, which estimates the distance of a point to the solution set of an optimization problem using the optimality residual, is a powerful tool for the analysis of f…
A General Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM
Linning Xu, Feng Yin, Jiawei Zhang +2
Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays. The benchmark method using maximum likelihood (ML) estimation and gra…
A Proximal Alternating Direction Method of Multiplier for Linearly Constrained Nonconvex Minimization
Jiawei Zhang, Zhi-Quan Luo
Consider the minimization of a nonconvex differentiable function over a polyhedron. A popular primal-dual first-order method for this problem is to perform a gradient projection it…