Kiri Wagstaff

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David R. ThompsonJet Propulsion Laboratory, California Institute of TechnologyVerified email at jpl.nasa.gov
Steve ChienJPL Fellow, Jet Propulsion Laboratory, Calif. Inst. Tech.Verified email at jpl.nasa.gov
Claire CardieProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Gary DoranJet Propulsion LaboratoryVerified 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
Stefan SchroedlAtomwise, IncVerified email at atomwise.com
Lukas MandrakeJet Propulsion LaboratoryVerified email at jpl.nasa.gov
Randall WaythSKA-Low Senior Commissioning ScientistVerified email at skao.int
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
Marie desJardinsDean and Professor of Computer ScienceVerified email at simmons.edu
Hannah Rae KernerAssistant Professor, Arizona State UniversityVerified email at umd.edu
Ian DavidsonProfessor of Computer Science, University of California - DavisVerified email at cs.ucdavis.edu
Sugato BasuDistinguished Scientist at GoogleVerified 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
Terran LaneProfessor of Computer Science, University of New MexicoVerified email at cs.unm.edu
Heni Ben AmorAssociate Professor, Arizona State UniversityVerified email at asu.edu

Kiri Wagstaff

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Verified email at wkiri.com - Homepage

Machine Learning Geology Astronomy Education Library Science


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Constrained k-means clustering with background knowledge K Wagstaff, C Cardie, S Rogers, S Schrödl Icml 1, 577-584, 2001	3996	2001
Clustering with instance-level constraints K Wagstaff, C Cardie AAAI/IAAI 1097 (577-584), 197, 2000	897	2000
Constrained clustering: Advances in algorithms, theory, and applications S Basu, I Davidson, K Wagstaff Chapman and Hall/CRC, 2008	754	2008
Machine learning that matters K Wagstaff Proceedings of the Twenty-Ninth International Conference on Machine Learning …, 2012	405	2012
Mining GPS traces for map refinement S Schroedl, K Wagstaff, S Rogers, P Langley, C Wilson Data mining and knowledge Discovery 9, 59-87, 2004	331	2004
Noun phrase coreference as clustering C Cardie, K Wagstaff 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language …, 1999	324	1999
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	286	2006
Machine learning for science and society C Rudin, KL Wagstaff Machine Learning 95, 1-9, 2014	159	2014
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	157	2010
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	149	2001
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, e006, 2013	146	2013
Intelligent clustering with instance-level constraints KL Wagstaff Cornell University, 2002	138	2002
Clustering with missing values: no imputation required. K Wagstaff Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space …, 2004	114	2004
Alpha seeding for support vector machines D DeCoste, K Wagstaff Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	110	2000
Onboard autonomy on the intelligent payload experiment cubesat mission S Chien, J Doubleday, DR Thompson, KL Wagstaff, J Bellardo, C Francis, ... Journal of Aerospace Information Systems 14 (6), 307-315, 2017	102	2017
When is constrained clustering beneficial, and why KL Wagstaff, S Basu, I Davidson Ionosphere 58 (60.1), 62-63, 2006	92	2006
Active constrained clustering by examining spectral eigenvectors Q Xu, M desJardins, KL Wagstaff Discovery Science: 8th International Conference, DS 2005, Singapore, October …, 2005	82	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	73	2011
Deep mars: Cnn classification of mars imagery for the pds imaging atlas K Wagstaff, Y Lu, A Stanboli, K Grimes, T Gowda, J Padams Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	71	2018
Constrained Spectral Clustering under a Local Proximity Structure Assumption. Q Xu, Marie desJardins, K Wagstaff FLAIRS, 866-867, 2005	62	2005

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