12 papers
Rethinking Learnability in Offline Data-driven Optimization
Chao Qian, Chen-Guang Wang, Rong-Xi Tan +1
Black-Box Optimization (BBO) has broad applications, while traditional algorithms such as evolutionary algorithms and Bayesian optimization face efficiency challenges as real-world…
Learning Early-to-Final Solution Consistency for MILP Acceleration
Guanlin Li, Chengrui Gao, Chenguang Wang +6
Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making.…
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…
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…
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…
Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP Solvers
Xuanhao Pan, Chenguang Wang, Chaolong Ying +2
The ``Heatmap + Monte Carlo Tree Search (MCTS)'' paradigm has recently emerged as a prominent framework for solving the Travelling Salesman Problem (TSP). While considerable effort…
Towards Principled Task Grouping for Multi-Task Learning
Chenguang Wang, Xuanhao Pan, Tianshu Yu
Multi-task learning (MTL) aims to leverage shared information among tasks to improve learning efficiency and accuracy. However, MTL often struggles to effectively manage positive a…
Efficient Training of Multi-task Neural Solver for Combinatorial Optimization
Chenguang Wang, Zhang-Hua Fu, Pinyan Lu +1
Efficiently training a multi-task neural solver for various combinatorial optimization problems (COPs) has been less studied so far. Naive application of conventional multi-task le…