activity
20202026
most citedWhen Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence

3 citations · 10 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Heterogeneous Agentic Coevolution

Wenqian Ye, Ziwei Guan, Zi Yang +14

Recent advances in self-improving LLM agents have demonstrated the potential of AI systems that drive their own intelligence. However, many domain-specific applications rely on spe…

math.OC2022★ 1 cited

A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima

Daouda Sow, Kaiyi Ji, Ziwei Guan +1

Bilevel optimization has found extensive applications in modern machine learning problems such as hyperparameter optimization, neural architecture search, meta-learning, etc. While…

math.OC2021★ 3 cited

Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process

Tianjiao Li, Ziwei Guan, Shaofeng Zou +3

The problem of constrained Markov decision process (CMDP) is investigated, where an agent aims to maximize the expected accumulated discounted reward subject to multiple constraint…

cs.LG2021★ 2 cited

PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method

Ziwei Guan, Tengyu Xu, Yingbin Liang

Emphatic temporal difference (ETD) learning (Sutton et al., 2016) is a successful method to conduct the off-policy value function evaluation with function approximation. Although E…

cs.LG2020★ 3 cited

When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence

Ziwei Guan, Tengyu Xu, Yingbin Liang

Generative adversarial imitation learning (GAIL) is a popular inverse reinforcement learning approach for jointly optimizing policy and reward from expert trajectories. A primary q…

cs.LG2020★ 1 cited

Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack

Ziwei Guan, Kaiyi Ji, Donald J Bucci +4

The multi-armed bandit formalism has been extensively studied under various attack models, in which an adversary can modify the reward revealed to the player. Previous studies focu…