Lingkai Zu — accepted AI-conference papers

2026

Towards Achieving Optimal Strong Regret and Constraint Violation via Computational Efficient Model-free RL

Xiyue Peng, Lingkai Zu, Ziyu Shao, Xin Liu

ICML 2026poster

We study episodic constrained Markov decision processes (CMDPs) with linear function approximation, where the goal is to achieve strong regret and constraint violation guarantees without allowing error cancellations. Unlike the existing work, which focuses on either tabular CMDP or model-based reinf…

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2025

Triple-Optimistic Learning for Stochastic Contextual Bandits with General Constraints

Hengquan Guo, Lingkai Zu, Xin Liu

ICML 2025poster

We study contextual bandits with general constraints, where a learner observes contexts and aims to maximize cumulative rewards while satisfying a wide range of general constraints. We introduce the Optimistic$^3$ framework, a novel learning and decision-making approach that integrates optimistic de…

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