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© 2021

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part I

  • Frank Hutter
  • Kristian Kersting
  • Jefrey Lijffijt
  • Isabel Valera
Conference proceedings ECML PKDD 2020

Part of the Lecture Notes in Computer Science book series (LNCS, volume 12457)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12457)

Table of contents

  1. Front Matter
    Pages i-l
  2. Pattern Mining

    1. Front Matter
      Pages 1-1
    2. Florian Seiffarth, Tamás Horváth, Stefan Wrobel
      Pages 3-18
    3. Hugo M. Proença, Peter Grünwald, Thomas Bäck, Matthijs van Leeuwen
      Pages 19-35
    4. Arnold Hien, Samir Loudni, Noureddine Aribi, Yahia Lebbah, Mohammed El Amine Laghzaoui, Abdelkader Ouali et al.
      Pages 36-54
    5. Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
      Pages 55-71
  3. Clustering

    1. Front Matter
      Pages 73-73
    2. Longqi Yang, Liangliang Zhang, Yuhua Tang
      Pages 75-90
    3. Benjamin Schelling, Lena Greta Marie Bauer, Sahar Behzadi, Claudia Plant
      Pages 91-107
    4. Lele Cao, Sahar Asadi, Wenfei Zhu, Christian Schmidli, Michael Sjöberg
      Pages 108-124
    5. Richard Leibrandt, Stephan Günnemann
      Pages 125-142
  4. Privacy and Fairness

    1. Front Matter
      Pages 143-143
    2. Adi Akavia, Max Leibovich, Yehezkel S. Resheff, Roey Ron, Moni Shahar, Margarita Vald
      Pages 145-161
    3. David Solans, Battista Biggio, Carlos Castillo
      Pages 162-177
  5. (Social) Network Analysis and Computational Social Science

    1. Front Matter
      Pages 179-179
    2. Wenjie Feng, Shenghua Liu, Danai Koutra, Huawei Shen, Xueqi Cheng
      Pages 181-197
    3. Guilherme Borges, Flavio Figueiredo, Renato M. Assunção, Pedro O. S. Vaz-de-Melo
      Pages 198-215
    4. Li Ma, Mingding Liao, Xiaofeng Gao, Guoze Zhang, Qiang Yan, Guihai Chen
      Pages 216-231
    5. Lei Qi, Mohammed Khaleel, Wallapak Tavanapong, Adisak Sukul, David Peterson
      Pages 232-248
    6. Jason (Jiasheng) Zhang, Dongwon Lee
      Pages 249-265

Other volumes

  1. Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14–18, 2020, Proceedings
  2. Machine Learning and Knowledge Discovery in Databases
    European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part I
  3. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part II
  4. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III
  5. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IV
  6. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V

About these proceedings

Introduction

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.

The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings.

The volumes are organized in topical sections as follows:

Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion.

Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning.

Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics.

Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data.

Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

 

 

Keywords

artificial intelligence clustering algorithms computer vision correlation analysis data mining databases education evolutionary algorithms graph theory Human-Computer Interaction (HCI) image processing machine learning network protocols neural networks pattern recognition probability signal processing user interfaces

Editors and affiliations

  1. 1.Albert-Ludwigs-UniversitätFreiburgGermany
  2. 2.TU DarmstadtDarmstadtGermany
  3. 3.Ghent UniversityGhentBelgium
  4. 4.Saarland UniversitySaarbrückenGermany

Bibliographic information