84 citations · 213 across the 28 of their papers we have counts for
5 papers · 1 filter
Compute Time Scaling with Recursive Models for Combinatorial Optimization
Zhengxi Zhang, Paul Swoboda
We propose Tiny Recursive Models for Combinatorial Optimization (\ours{}), a general neural method for combinatorial optimization that scales both depth (how often we recursively i…
DOGE-Train: Discrete Optimization on GPU with End-to-end Training
Ahmed Abbas, Paul Swoboda
We present a fast, scalable, data-driven approach for solving relaxations of 0-1 integer linear programs. We use a combination of graph neural networks (GNN) and the Lagrange decom…
Structured Prediction Problem Archive
Paul Swoboda, Bjoern Andres, Andrea Hornakova +6
Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one pl…
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Michal Rolínek, Paul Swoboda, Dominik Zietlow +3
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contai…
MAP inference via Block-Coordinate Frank-Wolfe Algorithm
Paul Swoboda, Vladimir Kolmogorov
We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the ori…