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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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Keywords

Table of contents (53 papers)

  1. Applications

    1. GALG: Linking Addresses in Tracking Ecosystem Using Graph Autoencoder with Link Generation

      • Tianyu Cui, Gang Xiong, Chang Liu, Junzheng Shi, Peipei Fu, Gaopeng Gou
      Pages 270-285

    2. Automatic Grading of Student Code with Similarity Measurement

      • Dongxia Wang, En Zhang, Xuesong Lu
      Pages 286-301

    3. Meta Hierarchical Reinforced Learning to Rank for Recommendation: A Comprehensive Study in MOOCs

      • Yuchen Li, Haoyi Xiong, Linghe Kong, Rui Zhang, Dejing Dou, Guihai Chen
      Pages 302-317

    4. Recognizing Cognitive Load by a Hybrid Spatio-Temporal Causal Model from Multivariate Physiological Data

      • Zirui Yong, Guoxin Su, Xiaohu Li, Lingyun Sun, Zejian Li, Li Liu
      Pages 318-334

    5. Placing (Historical) Facts on a Timeline: A Classification Cum Coref Resolution Approach

      • Sayantan Adak, Altaf Ahmad, Aditya Basu, Animesh Mukherjee
      Pages 335-352

    6. ‘John Ate 5 Apples’ != ‘John Ate Some Apples’: Self-supervised Paraphrase Quality Detection for Algebraic Word Problems

      • Rishabh Gupta, V. Venktesh, Mukesh Mohania, Vikram Goyal
      Pages 353-369

    7. Looking Beyond the Past: Analyzing the Intrinsic Playing Style of Soccer Teams

      • Jeroen Clijmans, Maaike Van Roy, Jesse Davis
      Pages 370-385

    8. Recognizing Non-small Cell Lung Cancer Subtypes by a Constraint-Based Causal Network from CT Images

      • Zhengqiao Deng, Shuang Qian, Jing Qi, Li Liu, Bo Xu
      Pages 386-402

    9. Detection of ADHD Based on Eye Movements During Natural Viewing

      • Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk et al.
      Pages 403-418

    10. FFBDNet: Feature Fusion and Bipartite Decision Networks for Recommending Medication Combination

      • Zisen Wang, Ying Liang, Zhengjun Liu
      Pages 419-436

    11. Towards Federated COVID-19 Vaccine Side Effect Prediction

      • Jiaqi Wang, Cheng Qian, Suhan Cui, Lucas Glass, Fenglong Ma
      Pages 437-452

    12. MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks

      • Qi Cao, Renhe Jiang, Chuang Yang, Zipei Fan, Xuan Song, Ryosuke Shibasaki
      Pages 453-468

    13. EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting

      • Feng Xie, Zhong Zhang, Liang Li, Bin Zhou, Yusong Tan
      Pages 469-485

  2. Applications: Transportation

    1. Front Matter

      Pages 487-487
      Download chapter PDF

    2. Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories

      • Zhiwen Zhang, Hongjun Wang, Zipei Fan, Jiyuan Chen, Xuan Song, Ryosuke Shibasaki
      Pages 489-504

    3. Attention, Filling in the Gaps for Generalization in Routing Problems

      • Ahmad Bdeir, Jonas K. Falkner, Lars Schmidt-Thieme
      Pages 505-520

    4. Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems

      • Tom Mertens, Marwan Hassani
      Pages 521-537

    5. A Bayesian Markov Model for Station-Level Origin-Destination Matrix Reconstruction

      • Victor Amblard, Amir Dib, Noëlie Cherrier, Guillaume Barthe
      Pages 538-553

    6. BusWTE: Realtime Bus Waiting Time Estimation of GPS Missing via Multi-task Learning

      • Yuecheng Rong, Jun Liu, Zhilin Xu, Jian Ding, Chuangming Zhang, Jiaxiang Gao
      Pages 554-570

    7. PathOracle: A Deep Learning Based Trip Planner for Daily Commuters

      • Md. Tareq Mahmood, Mohammed Eunus Ali, Muhammad Aamir Cheema, Syed Md. Mukit Rashid, Timos Sellis
      Pages 571-586
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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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