4 papers
Differentiable Knapsack and Top-k Operators via Dynamic Programming
Germain Vivier-Ardisson, Michaël E. Sander, Axel Parmentier +1
Knapsack and Top-k operators are useful for selecting discrete subsets of variables. However, their integration into neural networks is challenging as they are piecewise constant,…
Learning with Local Search MCMC Layers
Germain Vivier-Ardisson, Mathieu Blondel, Axel Parmentier
Integrating combinatorial optimization layers into neural networks has recently attracted significant research interest. However, many existing approaches lack theoretical guarante…
Primal-dual algorithm for contextual stochastic combinatorial optimization
Louis Bouvier, Thibault Prunet, Vincent Leclère +1
This paper introduces a novel approach to contextual stochastic optimization, integrating operations research and machine learning to address decision-making under uncertainty. Tra…
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems
Pierre-Cyril Aubin-Frankowski, Yohann De Castro, Axel Parmentier +1
Many real-world decision problems require solving, again and again, combinatorial optimization instances drawn from a common distribution. A recent line of structured learning meth…