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Machine learning pipelines that include a combinatorial optimization layer can give surprisingly efficient heuristics for difficult combinatorial optimization problems.
Lipschitzian optimization without the Lipschitz constant
D. R. Jones, C. D. Perttunen, and B. E. Stuckman · 1993
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A recovering beam search algorithm for the one-machine dynamic total completion time scheduling problem
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Introduction to Statistical Learning Theory
Olivier Bousquet, Stéphane Boucheron, and Gábor Lugosi · 2004
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Two-stage stochastic matching and spanning tree problems: Polynomial instances and approximation
Bruno Escoffier, Laurent Gourvès, Jérôme Monnot, and Olivier Spanjaard · 2010
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Structured Learning and Prediction in Computer Vision
Sebastian Nowozin · 2010
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Mathematical Foundations of Supervised Learning
Michael M. Wolf · 2018
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Machine learning for combinatorial optimization: A methodological tour d’horizon
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Axel Parmentier and Vincent T’Kindt · 2021
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Efficient and Modular Implicit Differentiation
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Learning with Combinatorial Optimization Layers: A Probabilistic Approach, July 2022
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Learning to Approximate Industrial Problems by Operations Research Classic Problems
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Steven G. Johnson · 2021
Cited alongside, same era.
OptNet: Differentiable Optimization as a Layer in Neural Networks
Brandon Amos and J. Zico Kolter
Cited in the paper.
Learning with Fenchel-Young losses
Mathieu Blondel, André F. T. Martins, and Vlad Niculae
Cited in the paper.
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Ruben Martinez-Cantin
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Differentiation of Blackbox Combinatorial Solvers
Marin Vlastelica, Anselm Paulus, Vit Musil, Georg Martius, and Michal Rolinek
Cited in the paper.