74 citations · 104 across the 8 of their papers we have counts for
9 papers
ScienceFlow: A long-horizon agent for ML research, scientific discovery and beyond
Mingming Zhao, Jiqian Dong, Kangping Xu +16
Enabling LLM agents to sustain productive, stable, and goal-aligned research over extended horizons is a central challenge for autonomous machine learning and scientific discovery,…
Hierarchical Self-Improvement: A Framework for Task-Specific Evolvable Agent Harnesses
Tailin Zhou
Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the \emph{harness}---is typically tre…
Learning Design-Score Manifold to Guide Diffusion Models for Offline Optimization
Tailin Zhou, Zhilin Chen, Wenlong Lyu +3
Optimizing complex systems, from discovering therapeutic drugs to designing high-performance materials, remains a fundamental challenge across science and engineering, as the under…
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
Tailin Zhou, Jiadong Yu, Jun Zhang +1
This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce…
Mode Connectivity and Data Heterogeneity of Federated Learning
Tailin Zhou, Jun Zhang, Danny H. K. Tsang
Federated learning (FL) enables multiple clients to train a model while keeping their data private collaboratively. Previous studies have shown that data heterogeneity between clie…
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao, Zijian Li, Wenqiang Sun +6
Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a pr…
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality
Jiadong Yu, Ahmad Alhilal, Tailin Zhou +2
Metaverse applications such as virtual reality (VR) content streaming, require optimal resource allocation strategies for mobile edge computing (MEC) to ensure a high-quality user…
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Tailin Zhou, Zehong Lin, Jun Zhang +1
Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a…
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
Tailin Zhou, Jun Zhang, Danny H. K. Tsang
Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant perfo…