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Kaiqing Zhang

44 papers · 2018–2025 · 9 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (7) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (9) πŸ”¬ Deep Specialist (15) πŸ† Grand Slam 🀝 Dynamic Duo (17) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (152) ❓ The Questioner (5) ⚑ Prolific Year (7) πŸš€ Conference Pioneer πŸ’Ž Century Club (44) πŸ”₯ Unstoppable (8) πŸ“ˆ Trend Setter

Conferences

NIPS (17) ICML (8) ICLR (6) AISTATS (3) COLT (3) AAAI (2) JMLR (2) L4DC (2) ACL (1)

Top co-authors

Tamer Basar (17) Russ Tedrake (5) Asuman Ozdaglar (5) Max Simchowitz (5) Asuman E. Ozdaglar (4) Dongsheng Ding (4) Zhuoran Yang (4) Bin Hu (3) Weichao Mao (3) Abhishek Gupta (3)

Keywords

multi-agent reinforcement learning (13) zero-sum game (8) nash equilibrium (8) markov game (6) policy gradient (6) sample complexity (6) policy optimization (5) markov decision process (4) multi-agent system (4) function approximation (3) reinforcement learning (3) natural policy gradient (3) stochastic game (3) sample efficiency (3) convergence guarantee (2) upper confidence bound (2) multi-agent learning (2) model-based reinforcement learning (2) offline reinforcement learning (2) constrained optimization (2)

Papers

Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs JMLR 2025
Do LLM Agents Have Regret? A Case Study in Online Learning and Games ICLR 2025
MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning ACL 2025
Foundations of Multi-Agent Learning in Dynamic Environments: Where Reinforcement Learning Meets Strategic Decision-Making AAAI 2025
Provable Partially Observable Reinforcement Learning with Privileged Information NIPS 2024
Robot Fleet Learning via Policy Merging ICLR 2024
Can Direct Latent Model Learning Solve Linear Quadratic Gaussian Control? L4DC 2023
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions NIPS 2023
Self-Supervised Reinforcement Learning that Transfers using Random Features NIPS 2023
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs NIPS 2023
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games NIPS 2023
Byzantine-Robust Online and Offline Distributed Reinforcement Learning AISTATS 2023
Symmetric (Optimistic) Natural Policy Gradient for Multi-Agent Learning with Parameter Convergence AISTATS 2023
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation COLT 2023
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective COLT 2023
The Complexity of Markov Equilibrium in Stochastic Games COLT 2023
Learning to Extrapolate: A Transductive Approach ICLR 2023
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? ICLR 2023
The Power of Regularization in Solving Extensive-Form Games ICLR 2023
Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing ICML 2023
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation ICML 2023
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity JMLR 2023
What is a Good Metric to Study Generalization of Minimax Learners? NIPS 2022
Globally Convergent Policy Search for Output Estimation NIPS 2022
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning ICML 2022
Do Differentiable Simulators Give Better Policy Gradients? ICML 2022
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence ICML 2022
Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs ICML 2021
Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates ICLR 2021
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity NIPS 2021
Decentralized Q-learning in Zero-sum Markov Games NIPS 2021
Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning AAAI 2021
Reinforcement Learning for Cost-Aware Markov Decision Processes ICML 2021
Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence L4DC 2020
Robust Multi-Agent Reinforcement Learning with Model Uncertainty NIPS 2020
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems NIPS 2020
Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes NIPS 2020
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods NIPS 2020
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis NIPS 2020
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity NIPS 2020
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games NIPS 2019
Non-Cooperative Inverse Reinforcement Learning NIPS 2019
Nonlinear Structured Signal Estimation in High Dimensions via Iterative Hard Thresholding AISTATS 2018
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents ICML 2018