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

Takuya Konishi, Takuro Fukunaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
EditorsFrank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030676636
Publication statusPublished - 2021
Externally publishedYes
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: Sept 14 2020Sept 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12459 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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