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Machine Learning, Optimization, and Data Science

9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part I

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Giuseppe Nicosia

    1. University of Catania, Catania, Italy

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  2. Varun Ojha

    1. Newcastle University, Newcastle upon Tyne, UK

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  3. Emanuele La Malfa

    1. University of Oxford, Oxford, UK

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  4. Gabriele La Malfa

    1. University of Cambridge, Cambridge, UK

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  5. Panos M. Pardalos

    1. University of Florida, Gainesville, USA

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  6. Renato Umeton

    1. Dana-Farber Cancer Institute, Boston, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14505)

Included in the following conference series:

Conference proceedings info: LOD 2023.

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Table of contents (37 papers)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Consensus-Based Participatory Budgeting for Legitimacy: Decision Support via Multi-agent Reinforcement Learning

    • Srijoni Majumdar, Evangelos Pournaras
    Pages 1-14

  3. Leverage Mathematics’ Capability to Compress and Generalize as Application of ML Embedding Extraction from LLMs and Its Adaptation in the Automotive Space

    • Vera Kalinichenko
    Pages 15-23

  4. Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks

    • Johannes Varga, Emil Karlsson, Günther R. Raidl, Elina Rönnberg, Fredrik Lindsten, Tobias Rodemann
    Pages 24-38

  5. Flocking Method for Identifying of Neural Circuits in Optogenetic Datasets

    • Margarita Zaleshina, Alexander Zaleshin
    Pages 39-52

  6. A Machine Learning Approach for Source Code Similarity via Graph-Focused Features

    • Giacomo Boldini, Alessio Diana, Vincenzo Arceri, Vincenzo Bonnici, Roberto Bagnara
    Pages 53-67

  7. Knowledge Distillation with Segment Anything (SAM) Model for Planetary Geological Mapping

    • Sahib Julka, Michael Granitzer
    Pages 68-77

  8. ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation

    • Abhijeet Pendyala, Justin Dettmer, Tobias Glasmachers, Asma Atamna
    Pages 78-92

  9. Perceptrons Under Verifiable Random Data Corruption

    • Jose E. Aguilar Escamilla, Dimitrios I. Diochnos
    Pages 93-103

  10. Dynamic Soaring in Uncertain Wind Conditions: Polynomial Chaos Expansion Approach

    • Jiří Novák, Peter Chudý
    Pages 104-115

  11. Solving Continuous Optimization Problems with a New Hyperheuristic Framework

    • Nándor Bándi, Noémi Gaskó
    Pages 116-130

  12. Benchmarking Named Entity Recognition Approaches for Extracting Research Infrastructure Information from Text

    • Georgios Cheirmpos, Seyed Amin Tabatabaei, Evangelos Kanoulas, Georgios Tsatsaronis
    Pages 131-141

  13. Genetic Programming with Synthetic Data for Interpretable Regression Modelling and Limited Data

    • Fitria Wulandari Ramlan, James McDermott
    Pages 142-157

  14. A FastMap-Based Framework for Efficiently Computing Top-K Projected Centrality

    • Ang Li, Peter Stuckey, Sven Koenig, T. K. Satish Kumar
    Pages 158-173

  15. Comparative Analysis of Machine Learning Models for Time-Series Forecasting of Escherichia Coli Contamination in Portuguese Shellfish Production Areas

    • Filipe Ferraz, Diogo Ribeiro, Marta B. Lopes, Sónia Pedro, Susana Vinga, Alexandra M. Carvalho
    Pages 174-188

  16. The Price of Data Processing: Impact on Predictive Model Performance

    • Gail Gilboa Freedman
    Pages 189-196

  17. Reward Shaping for Job Shop Scheduling

    • Alexander Nasuta, Marco Kemmerling, Daniel Lütticke, Robert H. Schmitt
    Pages 197-211

  18. A 3D Terrain Generator: Enhancing Robotics Simulations with GANs

    • Silvia Arellano, Beatriz Otero, Tomasz Piotr Kucner, Ramon Canal
    Pages 212-226

  19. Hybrid Model for Impact Analysis of Climate Change on Droughts in Indian Region

    • Ameya Gujar, Tanu Gupta, Sudip Roy
    Pages 227-242

  20. Bilevel Optimization by Conditional Bayesian Optimization

    • Vedat Dogan, Steven Prestwich
    Pages 243-258
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Other volumes

  1. Machine Learning, Optimization, and Data Science

  2. Machine Learning, Optimization, and Data Science

Keywords

About this book

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023. 

The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributions focus on the state of the art and the latest advances in the integration of machine learning, deep learning, nonlinear optimization and data science to provide and support the scientific and technological foundations for interpretable, explainable and trustworthy AI. 


Editors and Affiliations

  • University of Catania, Catania, Italy

    Giuseppe Nicosia

  • Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  • University of Oxford, Oxford, UK

    Emanuele La Malfa

  • University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  • University of Florida, Gainesville, USA

    Panos M. Pardalos

  • Dana-Farber Cancer Institute, Boston, USA

    Renato Umeton

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