4 Citaten (Scopus)

Samenvatting

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.

Originele taal-2English
TitelDecision-Focused Learning: Through the Lens of Learning to Rank
Plaats van productieBaltimore, Maryland, USA
UitgeverijProceedings of Machine Learning Research
Pagina's14935-14947
Aantal pagina's13
Volume162
StatusPublished - 17 jul 2022
EvenementThirty-eighth International Conference on Machine Learning - Baltimore, United States
Duur: 17 jul 202223 jul 2022
Congresnummer: 2022
https://icml.cc/virtual/2022/index.html

Publicatie series

NaamProceedings of Machine Learning Research

Conference

ConferenceThirty-eighth International Conference on Machine Learning
Verkorte titelICML
Land/RegioUnited States
StadBaltimore
Periode17/07/2223/07/22
Internet adres

Bibliografische nota

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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