7 citations · 30 across the 14 of their papers we have counts for
11 papers · 1 filter
Federated Multi-Level Optimization over Decentralized Networks
Shuoguang Yang, Xuezhou Zhang, Mengdi Wang
Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, s…
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
Jiacheng Guo, Zihao Li, Huazheng Wang +3
In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dim…
Representation Learning for General-sum Low-rank Markov Games
Chengzhuo Ni, Yuda Song, Xuezhou Zhang +2
We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on…
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Kaixuan Huang, Yu Wu, Xuezhou Zhang +4
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies follow…
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
Hui Yuan, Chengzhuo Ni, Huazheng Wang +4
Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances…
Byzantine-Robust Online and Offline Distributed Reinforcement Learning
Yiding Chen, Xuezhou Zhang, Kaiqing Zhang +2
We consider a distributed reinforcement learning setting where multiple agents separately explore the environment and communicate their experiences through a central server. Howeve…
Provable Benefits of Representational Transfer in Reinforcement Learning
Alekh Agarwal, Yuda Song, Wen Sun +3
We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used…
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
Xuezhou Zhang, Yuda Song, Masatoshi Uehara +3
We present BRIEE (Block-structured Representation learning with Interleaved Explore Exploit), an algorithm for efficient reinforcement learning in Markov Decision Processes with bl…
Representation Learning for Online and Offline RL in Low-rank MDPs
Masatoshi Uehara, Xuezhou Zhang, Wen Sun
This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL…
Corruption-Robust Offline Reinforcement Learning
Xuezhou Zhang, Yiding Chen, Jerry Zhu +1
We study the adversarial robustness in offline reinforcement learning. Given a batch dataset consisting of tuples , an adversary is allowed to arbitrarily modify …
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma +3
Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectat…