Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada, October 4–6, 2015, Proceedings

357 Pages·2015·22.65 MB·English

by  Nathalie Japkowicz

Nathalie Japkowicz
Stan Matwin (Eds.)
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18th International Conference, DS 2015
Banff, AB, Canada, October 4–6, 2015
Proceedings
123
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Lecture Notes in Arti cial Intelligence 9356
Subseries of Lecture Notes in Computer Science
LNAI Series Editors
Randy Goebel
University of Alberta, Edmonton, Canada
Yuzuru Tanaka
Hokkaido University, Sapporo, Japan
Wolfgang Wahlster
DFKI and Saarland University, Saarbrücken, Germany
LNAI Founding Series Editor
Joerg Siekmann
DFKI and Saarland University, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/1244
Nathalie Japkowicz Stan Matwin (Eds.)
(cid:129)
Discovery Science
18th International Conference, DS 2015
–
Banff, AB, Canada, October 4 6, 2015
Proceedings
123
Editors
Nathalie Japkowicz StanMatwin
University of Ottawa Faculty of Computer Science
Ottawa, ON Dalhousie University
Canada Halifax, NS
Canada
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Artificial Intelligence
ISBN 978-3-319-24281-1 ISBN978-3-319-24282-8 (eBook)
DOI 10.1007/978-3-319-24282-8
LibraryofCongressControlNumber:2015948779
LNCSSublibrary:SL7–ArtificialIntelligence
SpringerChamHeidelbergNewYorkDordrechtLondon
©SpringerInternationalPublishingSwitzerland2015
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Preface
This year’s International Conference on Discovery Science, DS 2015, was the 18th
event in this series. Like in previous years, the conference was co-located with the
InternationalConferenceonAlgorithmicLearningTheory,ALT2015,whichisalready
in its 26th year. Started in 2001, ALT/DS is one of the longest-running series of
co-located events in computer science. The unique combination of recent advances in
thedevelopmentandanalysisofmethodsfordiscoveringscientificknowledge,coming
from machine learning, data mining, and intelligent data analysis, as well as their
application in various scientific domains, on the one hand, with the algorithmic
advances in machine learning theory, on the other hand, makes every instance of this
jointeventuniqueandattractive.Thisvolumecontainsthepaperspresentedatthe18th
International Conference on Discovery Science, while the papers of the 26th Interna-
tional Conference on Algorithmic Learning Theory are published by Springer in a
companion volume (LNCS Vol. 9355).
The18thDiscoveryScienceconferencereceived44internationalsubmissions.Each
submissionwasreviewedbyatleasttwocommitteemembers.Thecommitteedecided
to accept 28 papers, of which 16 are long and 12 are short papers. This results in the
36% acceptance rate for long papers. As is the tradition of the Discovery Science and
the Algorithmic Learning Theory conferences, invited talks were shared between the
two meetings. This year’s DS invited talks were “Turning Prediction Tools into
Decision Tools” by Cynthia Rudin from MIT, and “Overcoming Obstacles to the
Adoption of Machine Learning by Domain Experts” by Kiri Wagstaff from Jet
Propulsion Laboratories, while the ALT invited talks were “Finding Hidden Structure
in Data with Tensor Decompositions” by Sham Kakade from Microsoft and the Uni-
versity of Washington, and “Bilinear Prediction Using Low Rank Models” by Inderjit
Dhillon form the University of Texas at Austin. Abstracts of all four invited talks are
included in these proceedings.
We would like to thank all authors of submitted papers, the Program Committee
members, and the additional reviewers for their efforts in evaluating the submitted
papers,aswellastheinvitedspeakersandtutorialpresenters.Supportandadvicefrom
RandyGoebel,theGeneralChairofbothconferences,wereessentialeverystepofthe
way. We are grateful to Kamalika Chaudhuri, Claudio Gentile, Sandra Zilles, and
Csaba Szepesvari for ensuring a smooth coordination with ALT. We are indebted to
Jonathan Amyot from the Faculty of Computer Science, Dalhousie University, for
putting up and maintaining our website with great competence and efficiency.
WearegratefultothepeoplebehindEasychairformakingthesystemavailablefree
of charge. It was an essential tool in the paper submission and evaluation process, as
wellasinthepreparationoftheSpringerproceedings.WearealsogratefultoSpringer
for their continuing support of Discovery Science and for publishing the conference
proceedings since its inception.
VI Preface
This year, both conferences were held on October 4-6 in the picturesque setting of
Banff, Alberta, and were organized by Sandra Zilles and Csaba Szepesvari. We are
verygratefultoISMCanada,anIBMcompany,totheAlbertaInnovates-Technology
Futures(AITF),totheCanadianArtificialIntelligenceAssociation(CAIAC),andtothe
FacultyofComputerScienceatDalhousieUniversityfortheirsponsorshipofboththe
conferences.
October 2015 Nathalie Japkowicz
Stan Matwin
Organization
Program Committee
Aijun An York University, Canada
Vincent Barnabe-Lortie University of Ottawa, Canada
Colin Bellinger University of Ottawa, Canada
Sabine Bergler Concordia University, Canada
Albert Bifet University of Waikato, New Zealand
Hendrik Blockeel K.U. Leuven, Belgium
Ivan Bratko University of Ljubljana, Slovenia
Michelangelo Ceci Università degli Studi di Bari, Italy
Tapio Elomaa Tampere University of Technology, Finland
Johannes Fürnkranz TU Darmstadt, Germany
Dragan Gamberger Rudjer Boskovic Institute, Croatia
Howard Hamilton University of Regina, Canada
Geoffrey Holmes University of Waikato, New Zealand
Diana Inkpen University of Ottawa, Canada
Aminul Islam Dalhousie University, Canada
Nathalie Japkowicz SITE, University of Ottawa, Canada
Ross King University of Manchester, UK
Svetlana Kiritchenko NRC, Canada
William Klement UHN, University of Toronto, Canada
Philippe Langlais Université de Montréal, Canada
Guy Lapalme RALI-DIRO, Université de Montréal, Canada
Donato Malerba Università degli Studi di Bari “Aldo Moro”, Italy
Stan Matwin University of Ottawa, Canada
Robert Mercer The University of Western Ontario, Canada
Evangelos Milios Dalhousie University, Canada
Zoran Obradovic Temple University, USA
Bernhard Pfahringer University of Waikato, New Zealand
Fred Popowich Simon Fraser University, Canada
Doina Precup McGill University, Canada
Marko Robnik-Sikonja University of Ljubljana, FRI, Slovenia
Mohak Shah GE Global Research, USA
Marina Sokolova University of Ottawa and Institute for Big Data Analytics,
Canada
Jerzy Stefanowski Poznań Univeristy of Technology, Poland
Einoshin Suzuki Kyushu University, Japan
Maguelonne Teisseire Cemagref - UMR Tetis, France
Herna Viktor University of Ottawa, Canada
VIII Organization
Harry Zhang University of New Brunswick, Canada
Min-Ling Zhang Southeast University, China
Nur Zincir-Heywood Dalhousie University, Canada
Blaz Zupan University of Ljubljana, Slovenia
Additional Reviewers
Alharthi, Haifa Fass, Dan Kurup, Unmesh
Cao, Xi Hang Fok, Ricky Lanotte, Pasqua Fabiana
Christoff, Zoe Grinberg, Nastasiya Odilinye, Lydia
Corizzo, Roberto Han, Chao Olier, Ivan
Davoudi, Heidar Kirinde Gamaarachchige, Tofiloski, Milan
Ellert, Bradley Prasadith
Bilinear Prediction Using Low Rank Models
Inderjit S. Dhillon
Department of Computer Science
University ofTexas at Austin,Austin, USA
[email protected]
Linear prediction methods, such as linear regression and classification, form the
bread-and-butterofmodernmachinelearning.Theclassicalscenarioisthepresenceof
datawithmultiplefeaturesandasingletargetvariable.However,therearemanyrecent
scenarios,wheretherearemultipletargetvariables.Forexample,predictingbidwords
for a web page (where each bid word acts as a target variable), or predicting diseases
linked to a gene. In many of these scenarios, the target variables might themselves be
associated with features. In these scenarios, we propose the use of bilinear prediction
with low-rank models. The low-rank models serve a dual purpose: (i) they enable
tractable computation even in the face of millions of data points as well as target
variables,and(ii)theyexploitcorrelationsamongthetargetvariables,evenwhenthere
aremanymissingobservations.Weillustrateourmethodologyontwomodernmachine
learning problems: multi-label learning and inductive matrix completion, and show
results on two applications: predicting Wikipedia labels, and predicting gene-disease
relationships.
This is joint work with Prateek Jain, Nagarajan Natarajan, Hsiang-Fu Yu and Kai
Zhong.