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cs.LG2026
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,…
cs.LG2025
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…
cs.LG2025
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…