Machine Learning, Optimization, and Data Science: 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part II | SpringerLink
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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 II

  • Conference proceedings
  • © 2024

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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 14506)

Included in the following conference series:

Conference proceedings info: LOD 2023.

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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. 


Keywords

Table of contents (34 papers)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Machine Learning, Optimization, and Data Science (LOD 2023)

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Integrated Human-AI Forecasting for Preventive Maintenance Task Duration Estimation

      • Jiye Li, Yun Yin, Daniel Lafond, Alireza Ghasemi, Claver Diallo, Eric Bertrand
      Pages 3-18

    3. Exploring Image Transformations with Diffusion Models: A Survey of Applications and Implementation Code

      • Silvia Arellano, Beatriz Otero, Ruben Tous
      Pages 19-33

    4. Geolocation Risk Scores for Credit Scoring Models

      • Erdem Ünal, Uğur Aydın, Murat Koraş, Barış Akgün, Mehmet Gönen
      Pages 34-44

    5. Social Media Analysis: The Relationship Between Private Investors and Stock Price

      • Zijun Liu, Xinxin Wu, Wei Yao
      Pages 45-54

    6. Deep Learning Model of Two-Phase Fluid Transport Through Fractured Media: A Real-World Case Study

      • Leonid Sheremetov, Luis A. Lopez-Peña, Gabriela B. Díaz-Cortes, Dennys A. Lopez-Falcon, Erick E. Luna-Rojero
      Pages 55-68

    7. A Proximal Algorithm for Network Slimming

      • Kevin Bui, Fanghui Xue, Fredrick Park, Yingyong Qi, Jack Xin
      Pages 69-83

    8. Diversity in Deep Generative Models and Generative AI

      • Gabriel Turinici
      Pages 84-93

    9. Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes

      • Piotr Pomorski, Denise Gorse
      Pages 94-108

    10. Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning

      • Raphael C. Engelhardt, Ralitsa Raycheva, Moritz Lange, Laurenz Wiskott, Wolfgang Konen
      Pages 109-123

    11. Alternating Mixed-Integer Programming and Neural Network Training for Approximating Stochastic Two-Stage Problems

      • Jan Kronqvist, Boda Li, Jan Rolfes, Shudian Zhao
      Pages 124-139

    12. Heaviest and Densest Subgraph Computation for Binary Classification. A Case Study

      • Zoltán Tasnádi, Noémi Gaskó
      Pages 140-148

    13. SMBOX: A Scalable and Efficient Method for Sequential Model-Based Parameter Optimization

      • Tarek Salhi, John Woodward
      Pages 149-162

    14. Accelerated Graph Integration with Approximation of Combining Parameters

      • Taehwan Yun, Myung Jun Kim, Hyunjung Shin
      Pages 163-176

    15. Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison

      • Moritz Lange, Noah Krystiniak, Raphael C. Engelhardt, Wolfgang Konen, Laurenz Wiskott
      Pages 177-191

    16. A Hybrid Steady-State Genetic Algorithm for the Minimum Conflict Spanning Tree Problem

      • Punit Kumar Chaubey, Shyam Sundar
      Pages 192-205

    17. Reinforcement Learning for Multi-Neighborhood Local Search in Combinatorial Optimization

      • Sara Ceschia, Luca Di Gaspero, Roberto Maria Rosati, Andrea Schaerf
      Pages 206-221

    18. Evaluation of Selected Autoencoders in the Context of End-User Experience Management

      • Sven Beckmann, Bernhard Bauer
      Pages 222-236

    19. Application of Multi-agent Reinforcement Learning to the Dynamic Scheduling Problem in Manufacturing Systems

      • David Heik, Fouad Bahrpeyma, Dirk Reichelt
      Pages 237-254
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Other volumes

  1. Machine Learning, Optimization, and Data Science

  2. Machine Learning, Optimization, and Data Science

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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