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Machine Learning and Knowledge Discovery in Databases

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

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Frank Hutter

    1. Albert-Ludwigs-Universität, Freiburg, Germany

  2. Kristian Kersting

    1. TU Darmstadt, Darmstadt, Germany

  3. Jefrey Lijffijt

    1. Ghent University, Ghent, Belgium

  4. Isabel Valera

    1. Saarland University, Saarbrücken, Germany

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12458)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2020.

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About this book

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.

 

 

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Table of contents (43 papers)

  1. Front Matter

    Pages i-xliii
    Download chapter PDF

  2. Deep Learning Optimization and Theory

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. ADMMiRNN: Training RNN with Stable Convergence via an Efficient ADMM Approach

      • Yu Tang, Zhigang Kan, Dequan Sun, Linbo Qiao, Jingjing Xiao, Zhiquan Lai et al.
      Pages 3-18

    3. Exponential Convergence of Gradient Methods in Concave Network Zero-Sum Games

      • Amit Kadan, Hu Fu
      Pages 19-34

    4. Adaptive Momentum Coefficient for Neural Network Optimization

      • Zana Rashidi, Kasra Ahmadi K. A., Aijun An, Xiaogang Wang
      Pages 35-51

    5. Squeezing Correlated Neurons for Resource-Efficient Deep Neural Networks

      • Elbruz Ozen, Alex Orailoglu
      Pages 52-68

    6. Activation Anomaly Analysis

      • Philip Sperl, Jan-Philipp Schulze, Konstantin Böttinger
      Pages 69-84

    7. Effective Version Space Reduction for Convolutional Neural Networks

      • Jiayu Liu, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers
      Pages 85-100

    8. A Principle of Least Action for the Training of Neural Networks

      • Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac, Patrick Gallinari
      Pages 101-117

  3. Active Learning

    1. Front Matter

      Pages 119-119
      Download chapter PDF

    2. Tackling Noise in Active Semi-supervised Clustering

      • Jonas Soenen, Sebastijan Dumančić, Toon Van Craenendonck, Hendrik Blockeel
      Pages 121-136

    3. A Taxonomy of Interactive Online Machine Learning Strategies

      • Agnes Tegen, Paul Davidsson, Jan A. Persson
      Pages 137-153

    4. Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing

      • Lu Yin, Vlado Menkovski, Mykola Pechenizkiy
      Pages 154-169

  4. Adversarial Learning

    1. Front Matter

      Pages 171-171
      Download chapter PDF

    2. Adversarial Learned Molecular Graph Inference and Generation

      • Sebastian Pölsterl, Christian Wachinger
      Pages 173-189

    3. A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI

      • Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu
      Pages 190-205

    4. Quality Guarantees for Autoencoders via Unsupervised Adversarial Attacks

      • Benedikt Böing, Rajarshi Roy, Emmanuel Müller, Daniel Neider
      Pages 206-222

    5. Metric Learning for Categorical and Ambiguous Features: An Adversarial Method

      • Xiaochen Yang, Mingzhi Dong, Yiwen Guo, Jing-Hao Xue
      Pages 223-238

    6. Learning Implicit Generative Models by Teaching Density Estimators

      • Kun Xu, Chao Du, Chongxuan Li, Jun Zhu, Bo Zhang
      Pages 239-255

    7. Reprogramming GANs via Input Noise Design

      • Kangwook Lee, Changho Suh, Kannan Ramchandran
      Pages 256-271
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Other volumes

  1. Machine Learning and Knowledge Discovery in Databases

  2. Machine Learning and Knowledge Discovery in Databases

  3. Machine Learning and Knowledge Discovery in Databases

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

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

  6. ECML PKDD 2020 Workshops

Editors and Affiliations

  • Albert-Ludwigs-Universität, Freiburg, Germany

    Frank Hutter

  • TU Darmstadt, Darmstadt, Germany

    Kristian Kersting

  • Ghent University, Ghent, Belgium

    Jefrey Lijffijt

  • Saarland University, Saarbrücken, Germany

    Isabel Valera

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