Kiri Wagstaff

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David R. ThompsonJet Propulsion Laboratory, California Institute of TechnologyVerified email at jpl.nasa.gov
Claire CardieProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
steve chienJPL Fellow, Jet Propulsion Laboratory, Calif. Inst. Tech.Verified email at jpl.nasa.gov
B. BueResearch Data Scientist, Jet Propulsion LaboratoryVerified email at jpl.nasa.gov
Umaa RebbapragadaJet Propulsion LaboratoryVerified email at jpl.nasa.gov
Randall WaythAssociate Professor. Curtin UniversityVerified email at curtin.edu.au
Adam DellerCentre for Astrophysics & Supercomputing, Swinburne University of TechnologyVerified email at astro.swin.edu.au
Steven TingayProfessor of Radio Astronomy, Curtin UniversityVerified email at curtin.edu.au
Stefan SchroedlAtomwise, IncVerified email at atomwise.com
Lukas MandrakeJet Propulsion LaboratoryVerified email at jpl.nasa.gov
Ian DavidsonProfessor of Computer Science, University of California - DavisVerified email at cs.ucdavis.edu
Sugato BasuPrincipal Scientist at GoogleVerified email at google.com
Marie desJardinsDean and Professor of Computer ScienceVerified email at simmons.edu
Dominic MazzoniGoogle, Inc.Verified email at google.com
Benyang TangJet Propulsion LaboratoryVerified email at jpl.nasa.gov
Thomas J. FuchsIcahn School of Medicine at Mount SinaiVerified email at mssm.edu
Dmitriy BekkerJohns Hopkins University Applied Physics LaboratoryVerified email at jhuapl.edu
Nathalie A. CabrolPlanetary Scientist, SETI Institute Carl Sagan Center/NASA Ames Research CenterVerified email at nasa.gov
Terran LaneProfessor of Computer Science, University of New MexicoVerified email at cs.unm.edu
William AbbeyNASA-JPLVerified email at jpl.nasa.gov

Kiri Wagstaff

Jet Propulsion Laboratory

Verified email at jpl.nasa.gov - Homepage

Machine Learning Geology Astronomy Astrobiology Education


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Constrained k-means clustering with background knowledge K Wagstaff, C Cardie, S Rogers, S Schroedl Icml 1, 577-584, 2001	3175	2001
Clustering with instance-level constraints K Wagstaff, C Cardie AAAI/IAAI 1097, 577-584, 2000	782	2000
Constrained clustering: Advances in algorithms, theory, and applications S Basu, I Davidson, K Wagstaff CRC Press, 2008	623	2008
Noun phrase coreference as clustering C Cardie, K Wagstaff 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language …, 1999	293	1999
Mining GPS traces for map refinement S Schroedl, K Wagstaff, S Rogers, P Langley, C Wilson Data mining and knowledge Discovery 9 (1), 59-87, 2004	276	2004
Measuring constraint-set utility for partitional clustering algorithms I Davidson, KL Wagstaff, S Basu European conference on principles of data mining and knowledge discovery …, 2006	253	2006
Machine learning that matters K Wagstaff arXiv preprint arXiv:1206.4656, 2012	241	2012
Multidocument summarization via information extraction M White, T Korelsky, C Cardie, V Ng, D Pierce, K Wagstaff Proceedings of the first international conference on Human language …, 2001	139	2001
Intelligent clustering with instance-level constraints KL Wagstaff Cornell University, 2002	122	2002
The commensal real-time ASKAP fast-transients (CRAFT) survey JP Macquart, M Bailes, NDR Bhat, GC Bower, JD Bunton, S Chatterjee, ... Publications of the Astronomical Society of Australia 27 (3), 272-282, 2010	103	2010
Alpha seeding for support vector machines D DeCoste, K Wagstaff Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	98	2000
VAST: an ASKAP survey for variables and slow transients T Murphy, S Chatterjee, DL Kaplan, J Banyer, ME Bell, HE Bignall, ... Publications of the Astronomical Society of Australia 30, 2013	88	2013
Clustering with missing values: No imputation required K Wagstaff Classification, clustering, and data mining applications, 649-658, 2004	87	2004
When is constrained clustering beneficial, and why? KL Wagstaff, S Basu, I Davidson Ionosphere 58 (60.1), 62-63, 2006	81	2006
Machine learning for science and society C Rudin, KL Wagstaff Machine Learning 95 (1), 1-9, 2014	80	2014
Active constrained clustering by examining spectral eigenvectors Q Xu, KL Wagstaff International Conference on Discovery Science, 294-307, 2005	78	2005
V-fastr: The vlba fast radio transients experiment RB Wayth, WF Brisken, AT Deller, WA Majid, DR Thompson, SJ Tingay, ... The Astrophysical Journal 735 (2), 97, 2011	65	2011
Constrained spectral clustering under a local proximity structure assumption Q Xu, M Desjardins, K Wagstaff In FLAIRS Conference, 2005	56	2005
Value, cost, and sharing: Open issues in constrained clustering KL Wagstaff International workshop on knowledge discovery in inductive databases, 1-10, 2006	53	2006
Forecasting space weather: Predicting interplanetary shocks using neural networks J Vandegriff, K Wagstaff, G Ho, J Plauger Advances in Space Research 36 (12), 2323-2327, 2005	52	2005

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