Skip to main content
SpringerLink
Log in
Book cover

Machine Learning and Knowledge Discovery in Databases. Research Track

European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part I

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Nuria Oliver

    1. ELLIS - The European Laboratory for Learning and Intelligent Systems, Alicante, Spain

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Fernando Pérez-Cruz

    1. ETHZ and EPFL, Zürich, Switzerland

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Stefan Kramer

    1. Johannes Gutenberg University of Mainz, Mainz, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Jesse Read

    1. École Polytechnique, Palaiseau, France

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  5. Jose A. Lozano

    1. Basque Center for Applied Mathematics, Bilbao, Spain

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12975)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2021.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (48 papers)

  1. Front Matter

    Pages i-xli
    Download chapter PDF

  2. Online Learning

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Routine Bandits: Minimizing Regret on Recurring Problems

      • Hassan Saber, Léo Saci, Odalric-Ambrym Maillard, Audrey Durand
      Pages 3-18

    3. Conservative Online Convex Optimization

      • Martino Bernasconi de Luca, Edoardo Vittori, Francesco Trovò, Marcello Restelli
      Pages 19-34

    4. Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits

      • Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth
      Pages 35-50

    5. Exploiting History Data for Nonstationary Multi-armed Bandit

      • Gerlando Re, Fabio Chiusano, Francesco Trovò, Diego Carrera, Giacomo Boracchi, Marcello Restelli
      Pages 51-66

    6. High-Probability Kernel Alignment Regret Bounds for Online Kernel Selection

      • Shizhong Liao, Junfan Li
      Pages 67-83

  3. Reinforcement Learning

    1. Front Matter

      Pages 85-85
      Download chapter PDF

    2. Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning

      • Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
      Pages 87-103

    3. Learning to Build High-Fidelity and Robust Environment Models

      • Weinan Zhang, Zhengyu Yang, Jian Shen, Minghuan Liu, Yimin Huang, Xing Zhang et al.
      Pages 104-121

    4. Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning

      • Muhammad Rizki Maulana, Wee Sun Lee
      Pages 122-138

    5. Multi-agent Imitation Learning with Copulas

      • Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon
      Pages 139-156

    6. CMIX: Deep Multi-agent Reinforcement Learning with Peak and Average Constraints

      • Chenyi Liu, Nan Geng, Vaneet Aggarwal, Tian Lan, Yuan Yang, Mingwei Xu
      Pages 157-173

    7. Model-Based Offline Policy Optimization with Distribution Correcting Regularization

      • Jian Shen, Mingcheng Chen, Zhicheng Zhang, Zhengyu Yang, Weinan Zhang, Yong Yu
      Pages 174-189

    8. Disagreement Options: Task Adaptation Through Temporally Extended Actions

      • Matthias Hutsebaut-Buysse, Tom De Schepper, Kevin Mets, Steven Latré
      Pages 190-205

    9. Deep Adaptive Multi-intention Inverse Reinforcement Learning

      • Ariyan Bighashdel, Panagiotis Meletis, Pavol Jancura, Gijs Dubbelman
      Pages 206-221

    10. Unsupervised Task Clustering for Multi-task Reinforcement Learning

      • Johannes Ackermann, Oliver Richter, Roger Wattenhofer
      Pages 222-237

    11. Deep Model Compression via Two-Stage Deep Reinforcement Learning

      • Huixin Zhan, Wei-Ming Lin, Yongcan Cao
      Pages 238-254

    12. Dropout’s Dream Land: Generalization from Learned Simulators to Reality

      • Zac Wellmer, James T. Kwok
      Pages 255-270

    13. Goal Modelling for Deep Reinforcement Learning Agents

      • Jonathan Leung, Zhiqi Shen, Zhiwei Zeng, Chunyan Miao
      Pages 271-286
Back to top

Other volumes

  1. Machine Learning and Knowledge Discovery in Databases. Research Track

  2. Machine Learning and Knowledge Discovery in Databases. Research Track

  3. Machine Learning and Knowledge Discovery in Databases. Research Track

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

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

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

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

Keywords

About this book

The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. 

The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions.

The volumes are organized in topical sections as follows:

Research Track:

Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications.

Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety.

Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics.

Applied Data Science Track:

Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation.

Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.

Editors and Affiliations

  • ELLIS - The European Laboratory for Learning and Intelligent Systems, Alicante, Spain

    Nuria Oliver

  • ETHZ and EPFL, Zürich, Switzerland

    Fernando Pérez-Cruz

  • Johannes Gutenberg University of Mainz, Mainz, Germany

    Stefan Kramer

  • École Polytechnique, Palaiseau, France

    Jesse Read

  • Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

Bibliographic Information

Publish with us

Policies and ethics

Societies and partnerships

Back to top

Search

Navigation