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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part VI

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Massih-Reza Amini

    1. Grenoble Alpes University, Saint Martin d'Hères, France

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  2. Stéphane Canu

    1. INSA Rouen Normandy, Saint Etienne du Rouvray, France

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  3. Asja Fischer

    1. Ruhr-Universität Bochum, Bochum, Germany

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  4. Tias Guns

    1. KU Leuven, Leuven, Belgium

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  5. Petra Kralj Novak

    1. Central European University, Vienna, Austria

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  6. Grigorios Tsoumakas

    1. Aristotle University of Thessaloniki, Thessaloniki, Greece

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13718)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2022.

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About this book

The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.

The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.

The volumes are organized in topical sections as follows:

Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning;

Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems;

Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search;

Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .

Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability;

Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.

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Table of contents (53 papers)

  1. Front Matter

    Pages i-xlviii
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  2. Time Series

    1. Front Matter

      Pages 1-1
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    2. Few-Shot Forecasting of Time-Series with Heterogeneous Channels

      • Lukas Brinkmeyer, Rafael Rego Drumond, Johannes Burchert, Lars Schmidt-Thieme
      Pages 3-18

    3. Online Adaptive Multivariate Time Series Forecasting

      • Amal Saadallah, Hanna Mykula, Katharina Morik
      Pages 19-35

    4. U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting

      • Kiran Madhusudhanan, Johannes Burchert, Nghia Duong-Trung, Stefan Born, Lars Schmidt-Thieme
      Pages 36-52

    5. Learning Perceptual Position-Aware Shapelets for Time Series Classification

      • Xuan-May Le, Minh-Tuan Tran, Van-Nam Huynh
      Pages 53-69

    6. Finding Local Groupings of Time Series

      • Zed Lee, Marco Trincavelli, Panagiotis Papapetrou
      Pages 70-86

    7. TS-MIoU: A Time Series Similarity Metric Without Mapping

      • Azim Ahmadzadeh, Yang Chen, Krishna Rukmini Puthucode, Ruizhe Ma, Rafal A. Angryk
      Pages 87-102

  3. Financial Machine Learning

    1. Front Matter

      Pages 103-103
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    2. Distributional Correlation–Aware Knowledge Distillation for Stock Trading Volume Prediction

      • Lei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, Xu Sun
      Pages 105-120

    3. Banksformer: A Deep Generative Model for Synthetic Transaction Sequences

      • Kyle Nickerson, Terrence Tricco, Antonina Kolokolova, Farzaneh Shoeleh, Charles Robertson, John Hawkin et al.
      Pages 121-136

    4. Stock Trading Volume Prediction with Dual-Process Meta-Learning

      • Ruibo Chen, Wei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, Xu Sun
      Pages 137-153

    5. Uncertainty Awareness for Predicting Noisy Stock Price Movements

      • Yun-Hsuan Lien, Yu-Syuan Lin, Yu-Shuen Wang
      Pages 154-169

    6. A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain

      • Stefan Haas, Eyke Hüllermeier
      Pages 170-184

    7. Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection

      • David Winkel, Niklas Strauß, Matthias Schubert, Thomas Seidl
      Pages 185-200

  4. Applications

    1. Front Matter

      Pages 201-201
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    2. Waypoint Generation in Row-Based Crops with Deep Learning and Contrastive Clustering

      • Francesco Salvetti, Simone Angarano, Mauro Martini, Simone Cerrato, Marcello Chiaberge
      Pages 203-218

    3. Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting

      • Alexander Koebler, Ralf Gross, Florian Buettner, Ingo Thon
      Pages 219-235

    4. Is This Bug Severe? A Text-Cum-Graph Based Model for Bug Severity Prediction

      • Rima Hazra, Arpit Dwivedi, Animesh Mukherjee
      Pages 236-252

    5. Physically Invertible System Identification for Monitoring System Edges with Unobservability

      • Jingyi Yuan, Yang Weng
      Pages 253-269
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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

  6. Machine Learning and Knowledge Discovery in Databases

  7. Machine Learning and Principles and Practice of Knowledge Discovery in Databases

  8. Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Editors and Affiliations

  • Grenoble Alpes University, Saint Martin d'Hères, France

    Massih-Reza Amini

  • INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  • Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  • KU Leuven, Leuven, Belgium

    Tias Guns

  • Central European University, Vienna, Austria

    Petra Kralj Novak

  • Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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