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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I

  • Conference proceedings
  • © 2013

Overview

Editors:
  1. Hendrik Blockeel

    1. Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium

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  2. Kristian Kersting

    1. Fraunhofer IAIS, Department of Knowledge Discovery, University of Bonn, Sankt Augustin, Germany

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  3. Siegfried Nijssen

    1. LIACS, Universiteit Leiden, Leiden, The Netherlands

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  4. Filip Železný

    1. Department of Computer Science and Engineering, Czech Technical University, Prague 6, Czech Republic

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8188)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2013.

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Table of contents (43 papers)

  1. Front Matter

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  2. Reinforcement Learning

    1. A Cascaded Supervised Learning Approach to Inverse Reinforcement Learning

      • Edouard Klein, Bilal Piot, Matthieu Geist, Olivier Pietquin
      Pages 1-16

    2. Learning from Demonstrations: Is It Worth Estimating a Reward Function?

      • Bilal Piot, Matthieu Geist, Olivier Pietquin
      Pages 17-32

    3. Recognition of Agents Based on Observation of Their Sequential Behavior

      • Qifeng Qiao, Peter A. Beling
      Pages 33-48

    4. Learning Throttle Valve Control Using Policy Search

      • Bastian Bischoff, Duy Nguyen-Tuong, Torsten Koller, Heiner Markert, Alois Knoll
      Pages 49-64

    5. Model-Selection for Non-parametric Function Approximation in Continuous Control Problems: A Case Study in a Smart Energy System

      • Daniel Urieli, Peter Stone
      Pages 65-80

    6. Learning Graph-Based Representations for Continuous Reinforcement Learning Domains

      • Jan Hendrik Metzen
      Pages 81-96

    7. Regret Bounds for Reinforcement Learning with Policy Advice

      • Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
      Pages 97-112

    8. Exploiting Multi-step Sample Trajectories for Approximate Value Iteration

      • Robert Wright, Steven Loscalzo, Philip Dexter, Lei Yu
      Pages 113-128

  3. Markov Decision Processes

    1. Expectation Maximization for Average Reward Decentralized POMDPs

      • Joni Pajarinen, Jaakko Peltonen
      Pages 129-144

    2. Properly Acting under Partial Observability with Action Feasibility Constraints

      • Caroline P. Carvalho Chanel, Florent Teichteil-Königsbuch
      Pages 145-161

    3. Iterative Model Refinement of Recommender MDPs Based on Expert Feedback

      • Omar Zia Khan, Pascal Poupart, John Mark Agosta
      Pages 162-177

    4. Solving Relational MDPs with Exogenous Events and Additive Rewards

      • Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, Alan Fern
      Pages 178-193

    5. Continuous Upper Confidence Trees with Polynomial Exploration – Consistency

      • David Auger, Adrien Couëtoux, Olivier Teytaud
      Pages 194-209

  4. Active Learning and Optimization

    1. A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization

      • Ali Jalali, Javad Azimi, Xiaoli Fern, Ruofei Zhang
      Pages 210-224

    2. Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration

      • Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis
      Pages 225-240

    3. Greedy Confidence Pursuit: A Pragmatic Approach to Multi-bandit Optimization

      • Philip Bachman, Doina Precup
      Pages 241-256

    4. A Time and Space Efficient Algorithm for Contextual Linear Bandits

      • José Bento, Stratis Ioannidis, S. Muthukrishnan, Jinyun Yan
      Pages 257-272

    5. Knowledge Transfer for Multi-labeler Active Learning

      • Meng Fang, Jie Yin, Xingquan Zhu
      Pages 273-288

  5. Learning from Sequences

    1. Spectral Learning of Sequence Taggers over Continuous Sequences

      • Adrià Recasens, Ariadna Quattoni
      Pages 289-304
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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

Keywords

About this book

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

Editors and Affiliations

  • Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium

    Hendrik Blockeel

  • Fraunhofer IAIS, Department of Knowledge Discovery, University of Bonn, Sankt Augustin, Germany

    Kristian Kersting

  • LIACS, Universiteit Leiden, Leiden, The Netherlands

    Siegfried Nijssen

  • Department of Computer Science and Engineering, Czech Technical University, Prague 6, Czech Republic

    Filip Železný

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