3 citations · 5 across the 8 of their papers we have counts for
8 papers
From Dual Tracking to Clipping: Provably Faster Distributionally Robust Multi-Objective Optimization
Yufeng Yang, Fangning Zhuo, Ziyi Chen +2
Multi-objective optimization (MOO) has received growing attention in applications that require learning under multiple criteria. However, most existing MOO formulations do not expl…
Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality
Shaocong Ma, Ziyi Chen, Yi Zhou +1
The goal of robust constrained reinforcement learning (RL) is to optimize an agent's performance under the worst-case model uncertainty while satisfying safety or resource constrai…
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization
Ziyi Chen, Yi Zhou, Yingbin Liang +1
Various optimal gradient-based algorithms have been developed for smooth nonconvex optimization. However, many nonconvex machine learning problems do not belong to the class of smo…
Data Sampling Affects the Complexity of Online SGD over Dependent Data
Shaocong Ma, Ziyi Chen, Yi Zhou +2
Conventional machine learning applications typically assume that data samples are independently and identically distributed (i.i.d.). However, practical scenarios often involve a d…
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization
Ziyi Chen, Bhavya Kailkhura, Yi Zhou
Many important machine learning applications involve regularized nonconvex bi-level optimization. However, the existing gradient-based bi-level optimization algorithms cannot handl…
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning
Ziyi Chen, Shaocong Ma, Yi Zhou
Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm that has been widely used for model training in various machine learning applications, which aims to solve…
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization
Ziyi Chen, Zhengyang Hu, Qunwei Li +2
Gradient descent-ascent (GDA) is a widely used algorithm for minimax optimization. However, GDA has been proved to converge to stationary points for nonconvex minimax optimization,…
Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity
Ziyi Chen, Yi Zhou, Rongrong Chen
The finite-time convergence of off-policy TD learning has been comprehensively studied recently. However, such a type of convergence has not been well established for off-policy TD…