14 citations · 14 across the 6 of their papers we have counts for
3 papers · 1 filter
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…