PhD student, Shanghai Jiaotong University
4 papers at NeurIPS 2025
We propose a structured denoising diffusion model, StruDiCO, which incrementally constructs solutions through step-wise variable selection.
We develop a benchmark for the classic combinatorial optimization problems (TSP, ATSP, CVRP, MIS, MCl, MVC, MCut) with relevant datasets.
This study propose a comprehensive benchmark for evaluating ML-based SAT solvers on cryptographic problems.
