1 citations · 1 across the 8 of their papers we have counts for
8 papers
Coding Agents as Test-Suite Auditors: Finding What Official Suites Miss While Approaching What They Catch
Shuyang Xie, Shuxiao Xie, Feng Zhu +2
Online-judge verdicts and the datasets and benchmarks built on them are treated as ground truth for evaluating and training large language models for code. Yet prior audits have so…
Variance-Reduced Q-Learning over Static and Time-Varying Networks
Sreejeet Maity, Feng Zhu, Aritra Mitra +1
We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP). The agents can exchange informati…
Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management
Carol Xuan Long, David Simchi-Levi, Feng Zhu +3
This paper studies the performance and reliability of autonomous generative AI agents in multi-echelon supply chains using the MIT Beer Game. We examine how model choice, operation…
Unified Data Selection for LLM Reasoning
Xiaoyuan Li, Yubo Ma, Chengpeng Li +6
Effectively training Large Language Models (LLMs) for complex, long-CoT reasoning is often bottlenecked by the need for massive high-quality reasoning data. Existing methods are ei…
OPERA: An Agent for Image Restoration with End-to-End Joint Planning-Execution Optimization
Feng Zhu, Shuyang Xie, Yihan Zeng +2
Real-world image restoration is challenging due to complex and interacting mixed degradations. Recent agent-based approaches address this problem by composing multiple task-specifi…
A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms
Feng Zhu, Robert W. Heath, Aritra Mitra
Stochastic variance-reduced algorithms such as Stochastic Average Gradient (SAG) and SAGA, and their deterministic counterparts like the Incremental Aggregated Gradient (IAG) metho…
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
Feng Zhu, Aritra Mitra, Robert W. Heath
Motivated by collaborative reinforcement learning (RL) and optimization with time-correlated data, we study a generic federated stochastic approximation problem involving agent…
Towards Fast Rates for Federated and Multi-Task Reinforcement Learning
Feng Zhu, Robert W. Heath, Aritra Mitra
We consider a setting involving agents, where each agent interacts with an environment modeled as a Markov Decision Process (MDP). The agents' MDPs differ in their reward funct…