4 papers · 1 filter
Incomplete Data, Complete Dynamics: A Diffusion Approach
Zihan Zhou, Chenguang Wang, Hongyi Ye +2
Learning physical dynamics from data is a fundamental challenge in machine learning and scientific modeling. Real-world observational data are inherently incomplete and irregularly…
Sampling from Binary Quadratic Distributions via Stochastic Localization
Chenguang Wang, Kaiyuan Cui, Weichen Zhao +1
Sampling from binary quadratic distributions (BQDs) is a fundamental but challenging problem in discrete optimization and probabilistic inference. Previous work established theoret…
Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
Chenguang Wang, Xiaoyu Zhang, Kaiyuan Cui +3
Training neural samplers directly from unnormalized densities without access to target distribution samples presents a significant challenge. A critical desideratum in these settin…
Are Your Generated Instances Truly Useful? GenBench-MILP: A Benchmark Suite for MILP Instance Generation
Yidong Luo, Chenguang Wang, Dong Li +1
The proliferation of machine learning-based methods for Mixed-Integer Linear Programming (MILP) instance generation has surged, driven by the need for diverse training datasets. Ho…