Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon. Bengio, Y., Lodi, A., & Prouvost, A. arXiv:1811.06128 [cs, stat], March, 2020. 637 citations (Semantic Scholar/arXiv) [2023-02-27] arXiv: 1811.06128![Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art methodologies involve algorithmic decisions that either require too much computing time or are not mathematically well defined. Thus, machine learning looks like a promising candidate to effectively deal with those decisions. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
@article{bengio_machine_2020,
title = {Machine {Learning} for {Combinatorial} {Optimization}: a {Methodological} {Tour} d'{Horizon}},
shorttitle = {Machine {Learning} for {Combinatorial} {Optimization}},
url = {http://arxiv.org/abs/1811.06128},
abstract = {This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art methodologies involve algorithmic decisions that either require too much computing time or are not mathematically well defined. Thus, machine learning looks like a promising candidate to effectively deal with those decisions. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.},
language = {en},
urldate = {2022-01-19},
journal = {arXiv:1811.06128 [cs, stat]},
author = {Bengio, Yoshua and Lodi, Andrea and Prouvost, Antoine},
month = mar,
year = {2020},
note = {637 citations (Semantic Scholar/arXiv) [2023-02-27]
arXiv: 1811.06128},
keywords = {/unread, Computer Science - Machine Learning, Statistics - Machine Learning, ⛔ No DOI found},
}
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