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Abstract
In the last years, decision-focused learning, also known as predict-and-optimize, has received increasing attention. In this setting, the predictions of a machine learning model are used as estimated cost coefficients in the objective function of a discrete combinatorial optimization problem for decision making. Decision-focused learning proposes to train the ML models, often neural network models, by directly optimizing the quality of decisions made by the optimization solvers. Based on a recent work that proposed a noise contrastive estimation loss over a subset of the solution space, we observe that decision-focused learning can more generally be seen as a learning-to-rank problem, where the goal is to learn an objective function that ranks the feasible points correctly. This observation is independent of the optimization method used and of the form of the objective function. We develop pointwise, pairwise and listwise ranking loss functions, which can be differentiated in closed form given a subset of solutions. We empirically investigate the quality of our generic methods compared to existing decision-focused learning approaches with competitive results. Furthermore, controlling the subset of solutions allows controlling the runtime considerably, with limited effect on regret.
Original language | English |
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Title of host publication | Decision-Focused Learning: Through the Lens of Learning to Rank |
Place of Publication | Baltimore, Maryland, USA |
Publisher | Proceedings of Machine Learning Research |
Pages | 14935-14947 |
Number of pages | 13 |
Volume | 162 |
Publication status | Published - 17 Jul 2022 |
Event | Thirty-eighth International Conference on Machine Learning - Baltimore, United States Duration: 17 Jul 2022 → 23 Jul 2022 Conference number: 2022 https://icml.cc/virtual/2022/index.html |
Publication series
Name | Proceedings of Machine Learning Research |
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Conference
Conference | Thirty-eighth International Conference on Machine Learning |
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Abbreviated title | ICML |
Country/Territory | United States |
City | Baltimore |
Period | 17/07/22 → 23/07/22 |
Internet address |
Bibliographical note
Funding Information:This research was partially funded by the FWO Flanders project Data-driven logistics (FWO-S007318N), the ANID-Fondecyt Iniciacion grant no 11220864, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant No. 101002802, CHAT-Opt) and the Flemish Government under the “Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen” programme. We are also thankful to the reviewers for their insightful comments.
Funding Information:
This research was partially funded by the FWO Flanders project Data-driven logistics (FWO-S007318N), the ANIDFondecyt Iniciacion grant no 11220864, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant No. 101002802, CHAT-Opt) and the Flemish Government under the “Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen” programme. We are also thankful to the reviewers for their insightful comments.
Publisher Copyright:
Copyright © 2022 by the author(s)
Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.
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- 1 Talk or presentation at a conference
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Predict and Optimize: Through the Lens of Learning to Rank
Jayanta Mandi (Speaker), Victor Bucarey Lopez (Speaker), Maxime Albert Mulamba Ke Tchomba (Speaker) & Tias Guns (Speaker)
3 Jul 2022 → 6 Jul 2022Activity: Talk or presentation › Talk or presentation at a conference