15 citations · 18 across the 16 of their papers we have counts for
16 papers
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2
MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous velocity field of flow matching. Howeve…
Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs
Ruiquan Huang, Donghao Li, Yingbin Liang +1
Reinforcement learning (RL) is a fundamental framework for sequential decision-making, in which an agent learns an optimal policy through interactions with an unknown environment.…
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis
Ruiquan Huang, Donghao Li, Chengshuai Shi +2
This paper investigates a hybrid learning framework for reinforcement learning (RL) in which the agent can leverage both an offline dataset and online interactions to learn the opt…
How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias
Ruiquan Huang, Yingbin Liang, Jing Yang
Language recognition tasks are fundamental in natural language processing (NLP) and have been widely used to benchmark the performance of large language models (LLMs). These tasks…
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes
Ruiquan Huang, Yingbin Liang, Jing Yang
Distributionally robust offline reinforcement learning (RL) aims to find a policy that performs the best under the worst environment within an uncertainty set using an offline data…
Non-asymptotic Convergence of Training Transformers for Next-token Prediction
Ruiquan Huang, Yingbin Liang, Jing Yang
Transformers have achieved extraordinary success in modern machine learning due to their excellent ability to handle sequential data, especially in next-token prediction (NTP) task…
Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
Fengyu Gao, Ruiquan Huang, Jing Yang
We study the problems of differentially private federated online prediction from experts against both stochastic adversaries and oblivious adversaries. We aim to minimize the avera…
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
Ruiquan Huang, Yuan Cheng, Jing Yang +2
In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures among multiple MDPs has been shown to yield significant b…
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
Ruiquan Huang, Yingbin Liang, Jing Yang
The general sequential decision-making problem, which includes Markov decision processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at maximizing a cumula…
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
Donghao Li, Ruiquan Huang, Cong Shen +1
This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the…
Federated Linear Contextual Bandits with User-level Differential Privacy
Ruiquan Huang, Huanyu Zhang, Luca Melis +3
This paper studies federated linear contextual bandits under the notion of user-level differential privacy (DP). We first introduce a unified federated bandits framework that can a…
Differentially Private Wireless Federated Learning Using Orthogonal Sequences
Xizixiang Wei, Tianhao Wang, Ruiquan Huang +3
We propose a privacy-preserving uplink over-the-air computation (AirComp) method, termed FLORAS, for single-input single-output (SISO) wireless federated learning (FL) systems. Fro…
Non-stationary Reinforcement Learning under General Function Approximation
Songtao Feng, Ming Yin, Ruiquan Huang +3
General function approximation is a powerful tool to handle large state and action spaces in a broad range of reinforcement learning (RL) scenarios. However, theoretical understand…
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs
Yuan Cheng, Ruiquan Huang, Jing Yang +1
In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any gi…
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL
Ruiquan Huang, Jing Yang, Yingbin Liang
Reward-free reinforcement learning (RF-RL), a recently introduced RL paradigm, relies on random action-taking to explore the unknown environment without any reward feedback informa…
Federated Linear Contextual Bandits
Ruiquan Huang, Weiqiang Wu, Jing Yang +1
This paper presents a novel federated linear contextual bandits model, where individual clients face different -armed stochastic bandits coupled through common global parameters…