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Computer Science - Artificial Intelligence | Machine Learning (Editorial Board)

Machine Learning

Machine Learning

Editor-in-Chief: Peter A. Flach

ISSN: 0885-6125 (print version)
ISSN: 1573-0565 (electronic version)

Journal no. 10994

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Editor-in-Chief:

Peter Flach, University of Bristol

Editor for Special Issues:

Dragos D. Margineantu, Boeing Research & Technology

Editors:

Bart Baesens, Catholic University of Leuven
Maria-Florina Balcan, Georgia Institute of Technology
Tijl De Bie, Ghent University
Hendrik Blockeel, Catholic University of Leuven
Karsten Borgwardt, ETH Zürich, Switzerland
Henrik Boström, Stockholm University
Ulf Brefeld, Yahoo! Research
Koby Crammer, Technion - Israel Institute of Technology
James Cussens, University of York
Hal Daumé III, University of Maryland
Jesse Davis, Katholieke Universiteit Leuven
Luc De Raedt, Catholic University of Leuven
Tapio Elomaa, Tampere University of Technology
Tom Fawcett, Silicon Valley Data Science
Alan Fern, Oregon State University
Xiaoli Fern, Oregon State University
Paolo Frasconi, University of Florence
Johannes Fürnkranz, TU Darmstadt
João Gama, University of Porto
Thomas Gärtner, University of Nottingham
Eyke Hüllermeier, University of Marburg
Thorsten Joachims, Cornell University
Kristian Kersting, TU Dortmund University
Roni Khardon, Tufts University
Cynthia Rudin, Massachusetts Institute of Technology
Csaba Szepesvari, University of Alberta
Prasad Tadepalli, Oregon State University
Jean-Philippe Vert, Mines ParisTech
Tong Zhang, Rutgers University
Jerry Zhu, University of Wisconsin-Madison

Editorial Board:

David Aha, Naval Research Laboratory
Yasemin Altun, Max Planck Institute for Biological Cybernetics
Peter Auer, University of Leoben
J. Andrew Bagnell, Carnegie Mellon University
Samy Bengio, Google, Inc.
David M. Blei, Princeton University
Avrim Blum, Carnegie Mellon University
Michael Bowling, University of Alberta
Pavel Brazdil, University of Porto
Carla Brodley, Tufts University
Claire Cardie, Cornell University
Nicolò Cesa-Bianchi, University of Milan
Olivier Chapelle, Yahoo! Research
William Cohen, Carnegie Mellon University
Corinna Cortes, Google Research, NY
Mark Craven, University of Wisconsin - Madison
Kenneth DeJong, George Mason University
Inderjit Dhillon, University of Texas at Austin
Pedro Domingos, University of Washington
Kurt Driessens, Catholic University of Leuven
Jennifer Dy, Northeastern University
Saso Dzeroski, Jozef Stefan Institute
Theodoros Evgeniou, INSEAD
Eibe Frank, University of Waikato
Gemma C. Garriga, University of Paris VI
Johannes Gehrke, Cornell University
Claudio Gentile, University of Insubria
Lise Getoor, University of Maryland
Zoubin Ghahramani, University of Cambridge
Mohammed Ghavamzadeh, INRIA Lille - Team SequeL
Amir Globerson, The Hebrew University of Jerusalem
Geoff Gordon, Carnegie Mellon University
Thore Graepel, Microsoft Research Ltd
Russ Greiner, University of Alberta
Steve Hanneke, Carnegie Mellon University
Elad Hazan, Technion, Israel
Tamas Horvath, University of Bonn and Fraunhofer IAIS
Marcus Hutter, Australian National University
Alexander Ihler, University of California - Irvine
Tony Jebara, Columbia University
David Jensen, University of Massachusetts
Adam Kalai, University of Chicago
Adam Klivans, University of Texas at Austin
Stefan Kramer, TU Munich
Niels Landwehr, University of Potsdam
Terran Lane, University of New Mexico
John Langford, Yahoo! Research
Pat Langley, Arizona State University and ISLE
Nada Lavrac, J. Stefan Institute
Francesca A. Lisi,  Università degli Studi di Bari "Aldo Moro"
Gábor Lugosi, Pompeu Fabra University
Sofus A. Macskassy, Fetch Technologies / USC
Donato Malerba, University of Bari
Shie Mannor, McGill University
Yishay Mansour, Tel-Aviv University
Amy McGovern, University of Oklahoma
Risto Miikkulainen, University of Texas at Austin
Brian Milch, Google, Inc.
Nina Mishra, Microsoft Research
Dunja Mladenic, Josef Stefan Institute
Mehryar Mohri, Courant Institute of Mathematical Sciences and Google Research
Claire Monteleoni, George Washington University
Stephen Muggleton, Imperial College London
Kevin Murphy, University of British Columbia
Jennifer Neville,  Purdue University
Alexandru Niculescu-Mizil, NEC Laboratories America
Siegfried Nijssen, Catholic University of Leuven
David Page, University of Wisconsin-Madison
Michael J. Pazzani, Rutgers University
Claudio Perlich, Media6Degrees
Bernhard Pfahringer, The University of Waikato
Massimiliano Pontil, University College London
Foster Provost, New York University
Alexander Rakhlin, University of Pennsylvania
Jan Ramon, Catholic University of Leuven
Carl Edward Rasmussen, University of Cambridge
Pradeep Ravikumar, University of Texas - Austin
Soumya Ray, Case Western Reserve University
Saharon Rosset, Tel Aviv University
Dan Roth, University of Illinois - Urbana/Champaign 
Maytal Saar-Tsechansky, The University of Texas at Austin
Lorenza Saitta, University of Piemonte Orientale
Claude Sammut, University of New South Wales
Scott Sanner, National ICT Australia
Robert Schapire, Princeton University
Tobias Scheffer, University of Potsdam
Jeff Schneider, Carnegie Mellon University
Dale Schuurmans, University of Waterloo
Stephen Scott, University of Nebraska
Michele Sebag, Université Paris-Sud
Fei Sha, University of Southern California
Shai Shalev-Shwartz, The Hebrew University of Jerusalem
Jude Shavlik, University of Wisconsin-Madison
John Shawe-Taylor, University College London
Ashwin Srinivasan, Oxford University
Gilles Stoltz, HEC Paris
Peter Stone, University of Texas at Austin
Yee-Whye Teh, University College London
Hannu Toivonen, University of Helsinki
Jean-Philippe Vert, Mines ParisTech
S.V.N. Vishwanathan, Purdue University
Kiri Wagstaff, Jet Propulsion Laboratory
Geoff Webb, Monash University
Max Welling, University of California, Irvine
Jason Weston, NEC Labs America
Shimon Whiteson, University of Amsterdam
Gerhard Widmer, Johannes Kepler University
Jennifer Wortman Vaughan, Microsoft, USA
Stefan Wrobel, Fraunhofer IAIS and University of Bonn
Eric Xing, Carnegie Mellon University
Filip Zelezny, Czech Technical University

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  • Aims and Scope

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    Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems, including but not limited to:

    Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.
    Learning Methods: Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods; Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction; Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning.

    Papers describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems (e.g., inherent complexity) or methods (e.g., relative performance of alternative algorithms) provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted. All papers must state their contributions clearly and describe how the contributions are supported. All papers must describe the supporting evidence in ways that can be verified or replicated by other researchers. All papers must describe the learning component clearly, and must discuss assumptions regarding knowledge representation and the performance task. All papers must place their contribution clearly in the context of existing work in machine learning. Variations from these prototypes, such as comprehensive surveys of active research areas, critical reviews of existing work, and book reviews, will be considered provided they make a clear contribution to the field.
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