6 citations · 16 across the 15 of their papers we have counts for
16 papers
MESHA: Mechanism-Enforced Sequential Halving for Strategic Linear Bandits
Xin Li, Zixin Zhong
We design and analyze \underline{M}echanism-\underline{E}nforced \underline{S}equential \underline{HA}lving (MESHA), an algorithm for Best Arm Identification (BAI) in strategic lin…
Replay What Matters: Off-Policy Replay for Efficient LLM Reinforcement Unlearning
Zirui Pang, Chenlong Zhang, Haosheng Tan +3
LLM unlearning has emerged as a cost-effective alternative to full retraining for removing hazardous knowledge from pretrained models while preserving general utility. Recent RL-ba…
Learning Multi-Agent Communication Protocol: Study on Information Entropy Efficiency in MARL
Xinren Zhang, Zixin Zhong, Jiadong Yu
Multi-Agent Systems (MAS) have emerged as a fundamental paradigm for distributed problem-solving, where autonomous agents collaborate to achieve complex objectives. Within this fra…
Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR
Yuhang He, Haodong Wu, Siyi Liu +7
Reinforcement Learning with Verifiable Rewards (RLVR) improves the reasoning ability of Large Language Models (LLMs), but sparse outcome rewards make token-level credit assignment…
On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits
Yunlong Hou, Zixin Zhong, Vincent Y. F. Tan
We study a stochastic multi-armed bandit problem where an agent is granted a free exploration budget before regret accumulates, a setting not captured by the classic regret minimiz…
Learning Efficient Communication Protocols for Multi-Agent Reinforcement Learning
Xinren Zhang, Jiadong Yu, Zixin Zhong
Multi-Agent Systems (MAS) have emerged as a powerful paradigm for modeling complex interactions among autonomous entities in distributed environments. In Multi-Agent Reinforcement…
Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
We propose a {\em novel} piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepo…
Stochastic Gradient Succeeds for Bandits
Jincheng Mei, Zixin Zhong, Bo Dai +3
We show that the \emph{stochastic gradient} bandit algorithm converges to a \emph{globally optimal} policy at an rate, even with a \emph{constant} step size. Remarkably, g…
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
Motivated by concerns about making online decisions that incur undue amount of risk at each time step, in this paper, we formulate the probably anytime-safe stochastic combinatoria…
Fast Beam Alignment via Pure Exploration in Multi-armed Bandits
Yi Wei, Zixin Zhong, Vincent Y. F. Tan
The beam alignment (BA) problem consists in accurately aligning the transmitter and receiver beams to establish a reliable communication link in wireless communication systems. Exi…
Optimal Clustering with Bandit Feedback
Junwen Yang, Zixin Zhong, Vincent Y. F. Tan
This paper considers the problem of online clustering with bandit feedback. A set of arms (or items) can be partitioned into various groups that are unknown. Within each group, the…
Almost Optimal Variance-Constrained Best Arm Identification
Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong
We design and analyze VA-LUCB, a parameter-free algorithm, for identifying the best arm under the fixed-confidence setup and under a stringent constraint that the variance of the c…
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
We study the Pareto frontier of two archetypal objectives in multi-armed bandits, namely, regret minimization (RM) and best arm identification (BAI) with a fixed horizon. It is fol…
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
We consider a best arm identification (BAI) problem for stochastic bandits with adversarial corruptions in the fixed-budget setting of T steps. We design a novel randomized algorit…
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
We design and analyze CascadeBAI, an algorithm for finding the best set of items, also called an arm, within the framework of cascading bandits. An upper bound on the time comp…
Thompson Sampling Algorithms for Cascading Bandits
Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
Motivated by the pressing need for efficient optimization in online recommender systems, we revisit the cascading bandit model proposed by Kveton et al. (2015). While Thompson samp…