33 citations · 54 across the 12 of their papers we have counts for
13 papers
Interaction Is Not Necessary for Order-Optimal 1-Bit Mean Estimation
Jiachen Hu, Han Zhong
This paper is concerned with one-bit mean estimation, where each independent sample is represented by a single binary message. We consider distributions on with mean i…
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability
Jiachen Hu, Rui Ai, Han Zhong +4
Information asymmetry is a pervasive feature of multi-agent systems, especially evident in economics and social sciences. In these settings, agents tailor their actions based on pr…
New Sphere Packings from the Antipode Construction
Ruitao Chen, Jiachen Hu, Binghui Li +2
In this note, we construct non-lattice sphere packings in dimensions , , , , , , and , demonstrating record densities that surpass all previously docume…
On Limitation of Transformer for Learning HMMs
Jiachen Hu, Qinghua Liu, Chi Jin
Despite the remarkable success of Transformer-based architectures in various sequential modeling tasks, such as natural language processing, computer vision, and robotics, their ab…
Quantum Non-Identical Mean Estimation: Efficient Algorithms and Fundamental Limits
Jiachen Hu, Tongyang Li, Xinzhao Wang +3
We systematically investigate quantum algorithms and lower bounds for mean estimation given query access to non-identically distributed samples. On the one hand, we give quantum me…
ZeroSwap: Data-driven Optimal Market Making in DeFi
Viraj Nadkarni, Jiachen Hu, Ranvir Rana +3
Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity p…
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
Han Zhong, Jiachen Hu, Yecheng Xue +2
While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited. In particular, it remains elusive how to design pro…
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
Jiachen Hu, Han Zhong, Chi Jin +1
Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world. Sim-to-real transfer has been widely used in practice because it is oft…
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
Xiaoyu Chen, Jiachen Hu, Lin F. Yang +1
Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to cou…
Understanding Domain Randomization for Sim-to-real Transfer
Xiaoyu Chen, Jiachen Hu, Chi Jin +2
Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to…
Near-optimal Representation Learning for Linear Bandits and Linear RL
Jiachen Hu, Xiaoyu Chen, Chi Jin +2
This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation. We first consider the setting where we…
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
Xiaoyu Chen, Jiachen Hu, Lihong Li +1
Reinforcement learning (RL) in episodic, factored Markov decision processes (FMDPs) is studied. We propose an algorithm called FMDP-BF, which leverages the factorization structure…
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication
Yuanhao Wang, Jiachen Hu, Xiaoyu Chen +1
We study the problem of regret minimization for distributed bandits learning, in which agents work collaboratively to minimize their total regret under the coordination of a ce…