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Machine Learning and Knowledge Discovery in Databases. Research Track

European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part IV

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Albert Bifet

    1. LTCI, Télécom Paris, Palaiseau Cedex, France

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  2. Jesse Davis

    1. KU Leuven, Leuven, Belgium

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  3. Tomas Krilavičius

    1. Faculty of Informatics, Vytautas Magnus University, Akademija, Lithuania

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  4. Meelis Kull

    1. Institute of Computer Science, University of Tartu, Tartu, Estonia

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  5. Eirini Ntoutsi

    1. Department of Computer Science, Bundeswehr University Munich, Munich, Germany

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  6. Indrė Žliobaitė

    1. Department of Computer Science, University of Helsinki, Helsinki, Finland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14944)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ECML PKDD 2024.

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About this book

This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024.

 

The papers presented in these proceedings are from the following three conference tracks: -

Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII.

 

Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII.

 

Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X.

Keywords

Table of contents (26 papers)

  1. Front Matter

    Pages i-lvii
    Download chapter PDF

  2. Research Track

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Model Fusion via Neuron Transplantation

      • Muhammed Öz, Nicholas Kiefer, Charlotte Debus, Jasmin Hörter, Achim Streit, Markus Götz
      Pages 3-19

    3. Compressed Federated Reinforcement Learning with a Generative Model

      • Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
      Pages 20-37

    4. Walking Noise: On Layer-Specific Robustness of Neural Architectures Against Noisy Computations and Associated Characteristic Learning Dynamics

      • Hendrik Borras, Bernhard Klein, Holger Fröning
      Pages 38-55

    5. KAFÈ: Kernel Aggregation for FEderated

      • Pian Qi, Diletta Chiaro, Fabio Giampaolo, Francesco Piccialli
      Pages 56-71

    6. On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance

      • Guoqiang Zhang
      Pages 72-88

    7. Graph Attention Network with Relational Dynamic Factual Fusion for Knowledge Graph Completion

      • Mei Yu, Yilin Zuo, Wenbin Zhang, Mankun Zhao, Tianyi Xu, Yue Zhao et al.
      Pages 89-106

    8. Low-Hanging Fruit: Knowledge Distillation from Noisy Teachers for Open Domain Spoken Language Understanding

      • Cheng Chen, Bowen Xing, Ivor W. Tsang
      Pages 107-125

    9. The Price of Labelling: A Two-Phase Federated Self-learning Approach

      • Tahani Aladwani, Shameem Puthiya Parambath, Christos Anagnostopoulos, Fani Deligianni
      Pages 126-142

    10. Disentangled Representations for Continual Learning: Overcoming Forgetting and Facilitating Knowledge Transfer

      • Zhaopeng Xu, Qi Qin, Bing Liu, Dongyan Zhao
      Pages 143-159

    11. On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Loss

      • Mohammed Fellaji, Frédéric Pennerath, Brieuc Conan-Guez, Miguel Couceiro
      Pages 160-176

    12. Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification

      • Davide Italo Serramazza, Thach Le Nguyen, Georgiana Ifrim
      Pages 177-195

    13. Novel Node Category Detection Under Subpopulation Shift

      • Hsing-Huan Chung, Shravan Chaudhari, Yoav Wald, Xing Han, Joydeep Ghosh
      Pages 196-212

    14. SynODC: Utilizing the Syntactic Structure for Outlier Detection in Categorical Attributes

      • Arthur Zylinski, Abdulhakim A. Qahtan
      Pages 213-229

    15. FELIX: Automatic and Interpretable Feature Engineering Using LLMs

      • Simon Malberg, Edoardo Mosca, Georg Groh
      Pages 230-246

    16. Harnessing Superclasses for Learning from Hierarchical Databases

      • Nicolas Urbani, Sylvain Rousseau, Yves Grandvalet, Leonardo Tanzi
      Pages 247-265

    17. Approximation Error of Sobolev Regular Functions with Tanh Neural Networks: Theoretical Impact on PINNs

      • Benjamin Girault, Rémi Emonet, Amaury Habrard, Jordan Patracone, Marc Sebban
      Pages 266-282

    18. A Theoretically Grounded Extension of Universal Attacks from the Attacker’s Viewpoint

      • Jordan Patracone, Paul Viallard, Emilie Morvant, Gilles Gasso, Amaury Habrard, Stéphane Canu
      Pages 283-300

    19. Linear Modeling of the Adversarial Noise Space

      • Jordan Patracone, Lucas Anquetil, Yuan Liu, Gilles Gasso, Stéphane Canu
      Pages 301-317
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Other volumes

  1. Machine Learning and Knowledge Discovery in Databases. Research Track

  2. Machine Learning and Knowledge Discovery in Databases. Research Track

  3. Machine Learning and Knowledge Discovery in Databases. Research Track

  4. Machine Learning and Knowledge Discovery in Databases. Research Track

  5. Machine Learning and Knowledge Discovery in Databases. Research Track

  6. Machine Learning and Knowledge Discovery in Databases. Research Track

  7. Machine Learning and Knowledge Discovery in Databases. Research Track

  8. Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track

  9. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

  10. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

Editors and Affiliations

  • LTCI, Télécom Paris, Palaiseau Cedex, France

    Albert Bifet

  • KU Leuven, Leuven, Belgium

    Jesse Davis

  • Faculty of Informatics, Vytautas Magnus University, Akademija, Lithuania

    Tomas Krilavičius

  • Institute of Computer Science, University of Tartu, Tartu, Estonia

    Meelis Kull

  • Department of Computer Science, Bundeswehr University Munich, Munich, Germany

    Eirini Ntoutsi

  • Department of Computer Science, University of Helsinki, Helsinki, Finland

    Indrė Žliobaitė

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