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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Ulf Brefeld

    1. Leuphana University, Lüneburg, Germany

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  2. Elisa Fromont

    1. IRISA/Inria, Rennes, France

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  3. Andreas Hotho

    1. University of Würzburg, Würzburg, Germany

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  4. Arno Knobbe

    1. Leiden University, Leiden, The Netherlands

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  5. Marloes Maathuis

    1. ETH Zurich, Zurich, Switzerland

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  6. Céline Robardet

    1. Institut National des Sciences Appliquées, Villeurbanne, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11907)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2019.

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Table of contents (43 papers)

  1. Front Matter

    Pages i-xxvi
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  2. Supervised Learning

    1. Front Matter

      Pages 1-1
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    2. Exploiting the Earth’s Spherical Geometry to Geolocate Images

      • Mike Izbicki, Evangelos E. Papalexakis, Vassilis J. Tsotras
      Pages 3-19

    3. Continual Rare-Class Recognition with Emerging Novel Subclasses

      • Hung Nguyen, Xuejian Wang, Leman Akoglu
      Pages 20-36

    4. Unjustified Classification Regions and Counterfactual Explanations in Machine Learning

      • Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki
      Pages 37-54

    5. Shift Happens: Adjusting Classifiers

      • Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull
      Pages 55-70

    6. Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data

      • Jessa Bekker, Pieter Robberechts, Jesse Davis
      Pages 71-85

    7. Cost Sensitive Evaluation of Instance Hardness in Machine Learning

      • Ricardo B. C. Prudêncio
      Pages 86-102

    8. Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification

      • Mari-Liis Allikivi, Meelis Kull
      Pages 103-120

  3. Multi-label Learning

    1. Front Matter

      Pages 121-121
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    2. PP-PLL: Probability Propagation for Partial Label Learning

      • Kaiwei Sun, Zijian Min, Jin Wang
      Pages 123-137

    3. Neural Message Passing for Multi-label Classification

      • Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
      Pages 138-163

    4. Assessing the Multi-labelness of Multi-label Data

      • Laurence A. F. Park, Yi Guo, Jesse Read
      Pages 164-179

    5. Synthetic Oversampling of Multi-label Data Based on Local Label Distribution

      • Bin Liu, Grigorios Tsoumakas
      Pages 180-193

  4. Large-Scale Learning

    1. Front Matter

      Pages 195-195
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    2. Distributed Learning of Non-convex Linear Models with One Round of Communication

      • Mike Izbicki, Christian R. Shelton
      Pages 197-212

    3. SLSGD: Secure and Efficient Distributed On-device Machine Learning

      • Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
      Pages 213-228

    4. Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning

      • Robin Vogel, Aurélien Bellet, Stephan Clémençon, Ons Jelassi, Guillaume Papa
      Pages 229-245

  5. Deep Learning

    1. Front Matter

      Pages 247-247
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    2. Importance Weighted Generative Networks

      • Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson
      Pages 249-265
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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

  5. Machine Learning and Knowledge Discovery in Databases

Keywords

About this book

The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019.

The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track.

The contributions were organized in topical sections named as follows:

Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization.

Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing.

Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track.

Chapter "Incorporating Dependencies in Spectral Kernels for Gaussian Processes" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Editors and Affiliations

  • Leuphana University, Lüneburg, Germany

    Ulf Brefeld

  • IRISA/Inria, Rennes, France

    Elisa Fromont

  • University of Würzburg, Würzburg, Germany

    Andreas Hotho

  • Leiden University, Leiden, The Netherlands

    Arno Knobbe

  • ETH Zurich, Zurich, Switzerland

    Marloes Maathuis

  • Institut National des Sciences Appliquées, Villeurbanne, France

    Céline Robardet

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