1 citations · 1 across the 7 of their papers we have counts for
7 papers
Divergence-Suppressing Couplings for Rectified Flow
Yimeng Min, Carla P. Gomes
The promise of Rectified Flow rests on producing self-generated couplings whose trajectories are straight, or nearly so. In practice, trajectories generated by the base flow model…
Learning Unbiased Permutations via Flow Matching
Yimeng Min, Carla P. Gomes
Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn produce a single softened solution…
Graph Neural Networks are Heuristics
Yimeng Min, Carla P. Gomes
Graph neural networks are usually treated as auxiliaries for combinatorial optimization: they imitate algorithms, guide search, or supply scores to classical procedures. We show th…
Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization
Yimeng Min, Carla P. Gomes
We propose a non-autoregressive framework for the Travelling Salesman Problem where solutions emerge directly from learned permutations, without requiring explicit search. By apply…
Unsupervised Ordering for Maximum Clique
Yimeng Min, Carla P. Gomes
We propose an unsupervised approach for learning vertex orderings for the maximum clique problem by framing it within a permutation-based framework. We transform the combinatorial…
Unsupervised Learning for Quadratic Assignment
Yimeng Min, Carla P. Gomes
We introduce PLUME search, a data-driven framework that enhances search efficiency in combinatorial optimization through unsupervised learning. Unlike supervised or reinforcement l…
On Size and Hardness Generalization in Unsupervised Learning for the Travelling Salesman Problem
Yimeng Min, Carla P. Gomes
We study the generalization capability of Unsupervised Learning in solving the Travelling Salesman Problem (TSP). We use a Graph Neural Network (GNN) trained with a surrogate loss…