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Machine Learning and Knowledge Discovery in Databases: Research Track

European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Danai Koutra

    1. University of Michigan, Ann Arbor, USA

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  2. Claudia Plant

    1. University of Vienna, Vienna, Austria

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  3. Manuel Gomez Rodriguez

    1. Max Planck Institute for Software Systems, Kaiserslautern, Germany

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  4. Elena Baralis

    1. Politecnico di Torino, Turin, Italy

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  5. Francesco Bonchi

    1. CENTAI, Turin, Italy

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14170)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2023.

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Table of contents (41 papers)

  1. Front Matter

    Pages i-liv
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  2. Computer Vision

    1. Front Matter

      Pages 1-1
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    2. Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

      • Wenkai Chen, Chuang Zhu, Mengting Li
      Pages 3-19

    3. DCID: Deep Canonical Information Decomposition

      • Alexander Rakowski, Christoph Lippert
      Pages 20-35

    4. Negative Prototypes Guided Contrastive Learning for Weakly Supervised Object Detection

      • Yu Zhang, Chuang Zhu, Guoqing Yang, Siqi Chen
      Pages 36-51

    5. Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks

      • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
      Pages 52-68

    6. Make a Long Image Short: Adaptive Token Length for Vision Transformers

      • Qiqi Zhou, Yichen Zhu
      Pages 69-85

    7. Graph Rebasing and Joint Similarity Reconstruction for Cross-Modal Hash Retrieval

      • Dan Yao, Zhixin Li
      Pages 86-102

    8. ARConvL: Adaptive Region-Based Convolutional Learning for Multi-class Imbalance Classification

      • Shuxian Li, Liyan Song, Xiaoyu Wu, Zheng Hu, Yiu-ming Cheung, Xin Yao
      Pages 103-120

  3. Deep Learning

    1. Front Matter

      Pages 121-121
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    2. Binary Domain Generalization for Sparsifying Binary Neural Networks

      • Riccardo Schiavone, Francesco Galati, Maria A. Zuluaga
      Pages 123-140

    3. Efficient Hyperdimensional Computing

      • Zhanglu Yan, Shida Wang, Kaiwen Tang, Weng-Fai Wong
      Pages 141-155

    4. Rényi Divergence Deep Mutual Learning

      • Weipeng Fuzzy Huang, Junjie Tao, Changbo Deng, Ming Fan, Wenqiang Wan, Qi Xiong et al.
      Pages 156-172

    5. Is My Neural Net Driven by the MDL Principle?

      • Eduardo Brandao, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet, Marc Sebban
      Pages 173-189

    6. Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regression

      • Daan Roordink, Sibylle Hess
      Pages 190-205

    7. Learning Distinct Features Helps, Provably

      • Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
      Pages 206-222

    8. Continuous Depth Recurrent Neural Differential Equations

      • Srinivas Anumasa, Geetakrishnasai Gunapati, P. K. Srijith
      Pages 223-238

  4. Fairness

    1. Front Matter

      Pages 239-239
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    2. Mitigating Algorithmic Bias with Limited Annotations

      • Guanchu Wang, Mengnan Du, Ninghao Liu, Na Zou, Xia Hu
      Pages 241-258

    3. \(\mathrm FG^2AN\): Fairness-Aware Graph Generative Adversarial Networks

      • Zichong Wang, Charles Wallace, Albert Bifet, Xin Yao, Wenbin Zhang
      Pages 259-275
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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: Applied Data Science and Demo Track

  7. Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track

Keywords

About this book

The multi-volume set LNAI 14169 until  14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023.

The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. 

The volumes are organized in topical sections as follows:

Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality;   Clustering.

Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning.

Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning.

Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning.

Part V: ​Robustness; Time Series; Transfer and Multitask Learning.

Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval.

​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Editors and Affiliations

  • University of Michigan, Ann Arbor, USA

    Danai Koutra

  • University of Vienna, Vienna, Austria

    Claudia Plant

  • Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  • Politecnico di Torino, Turin, Italy

    Elena Baralis

  • CENTAI, Turin, Italy

    Francesco Bonchi

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