10 papers
GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs
Yidong Luo, Xuemin Chen, Chenguang Wang +3
Configuring the hyperparameters of Mixed-integer programming (MIP) solvers is a high-dimensional, instance-dependent optimization problem where suboptimal settings can degrade solv…
A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
Chenguang Wang, Tianshu Yu
We develop a unified theoretical framework for data-free one-step sampling from unnormalized target distributions based on Wasserstein gradient flows. For a broad class of standard…
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…
Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching
Chenguang Wang, Zihan Zhou, Lei Bai +1
Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries…
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…
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…