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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 VI

  • Conference proceedings
  • © 2024

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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. Dept. of Computer Science, University of Helsinki, Helsinki, Finland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14946)

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
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  2. Research Track

    1. Front Matter

      Pages 1-1
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    2. Rejection Ensembles with Online Calibration

      • Sebastian Buschjäger
      Pages 3-20
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    3. Lighter, Better, Faster Multi-source Domain Adaptation with Gaussian Mixture Models and Optimal Transport

      • Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac
      Pages 21-38
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    4. Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering

      • Boci Peng, Yongchao Liu, Xiaohe Bo, Sheng Tian, Baokun Wang, Chuntao Hong et al.
      Pages 39-56
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    5. HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness

      • Zeyuan Zhao, Qingqing Ge, Anfeng Cheng, Yiding Liu, Xiang Li, Shuaiqiang Wang
      Pages 57-73
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    6. Interpetable Target-Feature Aggregation for Multi-task Learning Based on Bias-Variance Analysis

      • Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
      Pages 74-91
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    7. The Simpler The Better: An Entropy-Based Importance Metric to Reduce Neural Networks’ Depth

      • Victor Quétu, Zhu Liao, Enzo Tartaglione
      Pages 92-108
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    8. Towards Few-Shot Self-explaining Graph Neural Networks

      • Jingyu Peng, Qi Liu, Linan Yue, Zaixi Zhang, Kai Zhang, Yunhao Sha
      Pages 109-126
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    9. Uplift Modeling Under Limited Supervision

      • George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang
      Pages 127-144
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    10. Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection

      • Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang
      Pages 145-162
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    11. Modeling Text-Label Alignment for Hierarchical Text Classification

      • Ashish Kumar, Durga Toshniwal
      Pages 163-179
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    12. Secure Aggregation Is Not Private Against Membership Inference Attacks

      • Khac-Hoang Ngo, Johan Östman, Giuseppe Durisi, Alexandre Graell i Amat
      Pages 180-198
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    13. Evaluating Negation with Multi-way Joins Accelerates Class Expression Learning

      • Nikolaos Karalis, Alexander Bigerl, Caglar Demir, Liss Heidrich, Axel-Cyrille Ngonga Ngomo
      Pages 199-216
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    14. LayeredLiNGAM: A Practical and Fast Method for Learning a Linear Non-gaussian Structural Equation Model

      • Hirofumi Suzuki
      Pages 217-233
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    15. Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties

      • A. V. Arun Kumar, Alistair Shilton, Sunil Gupta, Santu Rana, Stewart Greenhill, Svetha Venkatesh
      Pages 234-250
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    16. Enhancing LLM’s Reliability by Iterative Verification Attributions with Keyword Fronting

      • Yize Sui, Jing Ren, Huibin Tan, Huan Chen, Zhaoye Li, Ji Wang
      Pages 251-268
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    17. Reconstructing the Unseen: GRIOT for Attributed Graph Imputation with Optimal Transport

      • Richard Serrano, Charlotte Laclau, Baptiste Jeudy, Christine Largeron
      Pages 269-286
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    18. Introducing Total Harmonic Resistance for Graph Robustness Under Edge Deletions

      • Lukas Berner, Henning Meyerhenke
      Pages 287-302
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    19. Counterfactual-Based Root Cause Analysis for Dynamical Systems

      • Juliane Weilbach, Sebastian Gerwinn, Karim Barsim, Martin Fränzle
      Pages 303-319
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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

  • Dept. of Computer Science, University of Helsinki, Helsinki, Finland

    Indrė Žliobaitė

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