14 citations · 14 across the 6 of their papers we have counts for
6 papers
Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems
Mingzhou Fan, Siyuan Xu, Mingxuan Yuan
Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use. Recent systems increasingly organize these agents as graphs of specialized, interconnec…
Path-Guided Particle-based Sampling
Mingzhou Fan, Ruida Zhou, Chao Tian +1
Particle-based Bayesian inference methods by sampling from a partition-free target (posterior) distribution, e.g., Stein variational gradient descent (SVGD), have attracted signifi…
Understanding Uncertainty-based Active Learning Under Model Mismatch
Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou +3
Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected bas…
GFlowNet Training by Policy Gradients
Puhua Niu, Shili Wu, Mingzhou Fan +1
Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties. We here propose a new GFlowNet training framework, with po…
An Interpretable Boosting-based Predictive Model for Transformation Temperatures of Shape Memory Alloys
Sina Hossein Zadeh, Amir Behbahanian, John Broucek +7
In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the availabl…
Adaptive Group Testing with Mismatched Models
Mingzhou Fan, Byung-Jun Yoon, Francis J. Alexander +2
Accurate detection of infected individuals is one of the critical steps in stopping any pandemic. When the underlying infection rate of the disease is low, testing people in groups…