End-to-End Learning for Prediction and Optimization with Gradient Boosting

Takuya Konishi, Takuro Fukunaga

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Mathematical optimization is a fundamental tool in decision making. However, it is often difficult to obtain an accurate formulation of an optimization problem due to uncertain parameters. Machine learning frameworks are attractive to address this issue: we predict the uncertain parameters and then optimize the problem based on the prediction. Recently, end-to-end learning approaches to predict and optimize the successive problems have received attention in the field of both optimization and machine learning. In this paper, we focus on gradient boosting which is known as a powerful ensemble method, and develop the end-to-end learning algorithm with maximizing the performance on the optimization problems directly. Our algorithm extends the existing gradient-based optimization through implicit differentiation to the second-order optimization for efficiently learning gradient boosting. We also conduct computational experiments to analyze how the end-to-end approaches work well and show the effectiveness of our end-to-end approach.

本文言語英語
ホスト出版物のタイトルMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
編集者Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
出版社Springer Science and Business Media Deutschland GmbH
ページ191-207
ページ数17
ISBN(印刷版)9783030676636
DOI
出版ステータス出版済み - 2021
外部発表はい
イベントEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
継続期間: 9 14 20209 18 2020

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12459 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online
Period9/14/209/18/20

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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