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Learning and Intelligent Optimization

17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Meinolf Sellmann

    1. InsideOpt, Dover, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Kevin Tierney

    1. Bielefeld University, Bielefeld, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14286)

Included in the following conference series:

Conference proceedings info: LION 2023.

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About this book

This book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4–8, 2023.

The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.

Keywords

Table of contents (40 papers)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Anomaly Classification to Enable Self-healing in Cyber Physical Systems Using Process Mining

    • Uphar Singh, Deepak Gajjala, Rahamatullah Khondoker, Harshit Gupta, Ayush Sinha, O. P. Vyas
    Pages 1-15

  3. Hyper-box Classification Model Using Mathematical Programming

    • Georgios I. Liapis, Lazaros G. Papageorgiou
    Pages 16-30

  4. A Leak Localization Algorithm in Water Distribution Networks Using Probabilistic Leak Representation and Optimal Transport Distance

    • Andrea Ponti, Ilaria Giordani, Antonio Candelieri, Francesco Archetti
    Pages 31-45

  5. Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming

    • Konstantinos I. Chatzilygeroudis, Michael N. Vrahatis
    Pages 46-61

  6. Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing

    • Kunal Jain, K. J. Prabuchandran, Tejas Bodas
    Pages 62-77

  7. A Bayesian Optimization Algorithm for Constrained Simulation Optimization Problems with Heteroscedastic Noise

    • Sasan Amini, Inneke Van Nieuwenhuyse
    Pages 78-91

  8. Hierarchical Machine Unlearning

    • HongBin Zhu, YuXiao Xia, YunZhao Li, Wei Li, Kang Liu, Xianzhou Gao
    Pages 92-106

  9. Explaining the Behavior of Reinforcement Learning Agents Using Association Rules

    • Zahra Parham, Vi Tching de Lille, Quentin Cappart
    Pages 107-120

  10. Deep Randomized Networks for Fast Learning

    • Richárd Rádli, László Czúni
    Pages 121-134

  11. Generative Models via Optimal Transport and Gaussian Processes

    • Antonio Candelieri, Andrea Ponti, Francesco Archetti
    Pages 135-149

  12. Real-World Streaming Process Discovery from Low-Level Event Data

    • Franck Lefebure, Cecile Thuault, Stephane Cholet
    Pages 150-164

  13. Robust Neural Network Approach to System Identification in the High-Noise Regime

    • Elisa Negrini, Giovanna Citti, Luca Capogna
    Pages 165-178

  14. GPU for Monte Carlo Search

    • Lilian Buzer, Tristan Cazenave
    Pages 179-193

  15. Learning the Bias Weights for Generalized Nested Rollout Policy Adaptation

    • Julien Sentuc, Farah Ellouze, Jean-Yves Lucas, Tristan Cazenave
    Pages 194-207

  16. Heuristics Selection with ML in CP Optimizer

    • Hugues Juillé, Renaud Dumeur, Paul Shaw
    Pages 208-222

  17. Model-Based Feature Selection for Neural Networks: A Mixed-Integer Programming Approach

    • Shudian Zhao, Calvin Tsay, Jan Kronqvist
    Pages 223-238

  18. An Error-Based Measure for Concept Drift Detection and Characterization

    • Antoine Bugnicourt, Riad Mokadem, Franck Morvan, Nadia Bebeshina
    Pages 239-253

  19. Predict, Tune and Optimize for Data-Driven Shift Scheduling with Uncertain Demands

    • Michael Römer, Felix Hagemann, Till Frederik Porrmann
    Pages 254-269

  20. On Learning When to Decompose Graphical Models

    • Aleksandra Petrova, Javier Larrosa
    Pages 270-285
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Other volumes

  1. Learning and Intelligent Optimization

Editors and Affiliations

  • InsideOpt, Dover, USA

    Meinolf Sellmann

  • Bielefeld University, Bielefeld, Germany

    Kevin Tierney

Bibliographic Information

  • Book Title: Learning and Intelligent Optimization

  • Book Subtitle: 17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers

  • Editors: Meinolf Sellmann, Kevin Tierney

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-44505-7

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Softcover ISBN: 978-3-031-44504-0Published: 25 October 2023

  • eBook ISBN: 978-3-031-44505-7Published: 24 October 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIV, 616

  • Number of Illustrations: 30 b/w illustrations, 142 illustrations in colour

  • Topics: Computational Mathematics and Numerical Analysis

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