2 citations · 2 across the 5 of their papers we have counts for
6 papers
Decentralized Asynchronous Multi-player Bandits
Jingqi Fan, Canzhe Zhao, Shuai Li +1
In recent years, multi-player multi-armed bandits (MP-MAB) have been extensively studied due to their wide applications in cognitive radio networks and Internet of Things systems.…
Heavy-tailed Linear Bandits: Adversarial Robustness, Best-of-both-worlds, and Beyond
Canzhe Zhao, Shinji Ito, Shuai Li
Heavy-tailed bandits have been extensively studied since the seminal work of \citet{Bubeck2012BanditsWH}. In particular, heavy-tailed linear bandits, enabling efficient learning wi…
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
Canzhe Zhao, Ruofeng Yang, Baoxiang Wang +2
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting. In particular, the unknown transition probability kernel admits…
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning
Canzhe Zhao, Yanjie Ze, Jing Dong +2
Communication lays the foundation for cooperation in human society and in multi-agent reinforcement learning (MARL). Humans also desire to maintain their privacy when communicating…
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization
Canzhe Zhao, Yanjie Ze, Jing Dong +2
Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning. While many TD learning methods have been developed in recent years, little…
Conservative Contextual Combinatorial Cascading Bandit
Kun Wang, Canzhe Zhao, Shuai Li +1
Conservative mechanism is a desirable property in decision-making problems which balance the tradeoff between the exploration and exploitation. We propose the novel \emph{conservat…