1 citations · 1 across the 14 of their papers we have counts for
19 papers
Variance-Aware Fine-Grained Gap-Dependent Bounds for Online Reinforcement Learning
Haochen Zhang, Lingzhou Xue, Zhong Zheng
We study model-free online reinforcement learning (RL) for episodic tabular Markov decision processes, focusing on both gap-dependent regret and policy switching cost. While fine-g…
Exact Fast Batch Simulation for Tabular Reinforcement Learning
Haochen Zhang, Lingzhou Xue, Zhong Zheng
Simulation is a fundamental computational primitive in reinforcement learning (RL), yet conventional simulation explicitly generates individual trajectories even when downstream pr…
MLToolBench: Learning Tool-Augmented Agents for Machine Learning Development
Xin Yu, Lizhu Zhang, Jiamu Bai +7
Machine learning engineering (MLE) agents have made substantial progress, but learning through ML experimentation remains costly in time and computation. Synthetic environments red…
Provably Efficient Federated Reinforcement Learning with Linear Function Approximation and Logarithmic Communication Cost
Zihang Liang, Haochen Zhang, Lingzhou Xue
We study federated online reinforcement learning with linear function approximation. While recent multi-agent reinforcement learning algorithms achieve strong regret guarantees, th…
Difficulty-Aware Semantic-ID Optimization for Generative Recommendation
Xin Yu, Stephen Li, Sina Aghaei +6
Semantic-ID-based generative recommendation casts retrieval and ranking as autoregressive generation over hierarchical item identifiers. A common recipe is SFT followed by GRPO, ye…
Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization
Xin Yu, Liuchen Liao, Yiwen Zhang +3
On-policy distillation is an efficient alternative to reinforcement learning, offering dense token-level training signals. However, its reliance on a stronger external teacher has…
Understanding Gap-Dependent Regret for Optimism-Based Reinforcement Learning with Linear Function Approximation
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We study gap-dependent regret for reinforcement learning with linear function approximation. While prior works have established gap-dependent guarantees in this setting, existing a…
EXACT: Explicit Attribute-Guided Decoding-Time Personalization
Xin Yu, Hanwen Xing, Lingzhou Xue
Achieving personalized alignment requires adapting large language models to each user's evolving context. While decoding-time personalization offers a scalable alternative to train…
Q-Learning with Fine-Grained Gap-Dependent Regret
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minima…
Train Large, Deploy Compact: Structured Compression for Compact Low-Rank Adaptation
Xin Yu, Cong Xie, Xunmei Liu +3
Low-rank adaptation (LoRA) has become a widely used paradigm for parameter-efficient fine-tuning of large language models, yet its representational capacity often lags behind full…
A New Inexact Manifold Proximal Linear Algorithm with Adaptive Stopping Criteria
Zhong Zheng, Xin Yu, Shiqian Ma +1
This paper proposes a new inexact manifold proximal linear (IManPL) algorithm for solving nonsmooth, nonconvex composite optimization problems over an embedded submanifold. At each…
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-polic…
AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
Xin Yu, Yujia Wang, Jinghui Chen +1
Low-Rank Adaptation (LoRA) has emerged as an effective technique for reducing memory overhead in fine-tuning large language models. However, it often suffers from sub-optimal perfo…
Gap-Dependent Bounds for Federated -learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We present the first gap-dependent analysis of regret and communication cost for on-policy federated -Learning in tabular episodic finite-horizon Markov decision processes (MDPs…
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition
Zhong Zheng, Haochen Zhang, Lingzhou Xue
We study the gap-dependent bounds of two important algorithms for on-policy Q-learning for finite-horizon episodic tabular Markov Decision Processes (MDPs): UCB-Advantage (Zhang et…
Smoothed Robust Phase Retrieval
Zhong Zheng, Lingzhou Xue
The phase retrieval problem in the presence of noise aims to recover the signal vector of interest from a set of quadratic measurements with infrequent but arbitrary corruptions, a…
Adaptive Algorithms for Robust Phase Retrieval
Zhong Zheng, Necdet Serhat Aybat, Shiqian Ma +1
This paper considers robust phase retrieval, which can be cast as a nonsmooth and nonconvex optimization problem. We propose two first-order algorithms with adaptive step sizes: th…
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost
Zhong Zheng, Haochen Zhang, Lingzhou Xue
In this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes. Under the coordination of a central server, multiple agents c…
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
Zhong Zheng, Fengyu Gao, Lingzhou Xue +1
In this paper, we consider federated reinforcement learning for tabular episodic Markov Decision Processes (MDP) where, under the coordination of a central server, multiple agents…