Jennifer Wortman Vaughan

Cited by

	All	Since 2016
Citations	5740	3868
h-index	33	25
i10-index	52	44

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200620072008200920102011201220132014201520162017201820192020202116 26 73 104 146 186 222 284 305 404 420 496 553 894 1293 193

Co-authors

Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Koby CrammerProfessor of Electrical Engineering, Technion - Israel Institute of TechnologyVerified email at ee.technion.ac.il
Yiling ChenHarvard UniversityVerified email at seas.harvard.edu
Chien-Ju HoWashington University in St. LouisVerified email at wustl.edu
Hanna WallachSenior Principal Researcher, Microsoft ResearchVerified email at dirichlet.net
Aleksandrs SlivkinsMicrosoft Research New YorkVerified email at microsoft.com
David M PennockProfessor, Rutgers UniversityVerified email at dimacs.rutgers.edu
Fernando PereiraVP and Engineering Fellow, GoogleVerified email at google.com
Alex KuleszaResearch Scientist, GoogleVerified email at google.com
John BlitzerGoogle ResearchVerified email at google.com
Eyal Even-DarFinalVerified email at final.co.il
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Miroslav DudikMicrosoft ResearchVerified email at microsoft.com
Yoav ShohamProfessor (emeritus)Verified email at cs.stanford.edu
Shai Ben-DavidUniverity of Waterloo, CanadaVerified email at uwaterloo.ca
Jacob AbernethyAssistant Professor, University of MichiganVerified email at umich.edu
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandVerified email at umiacs.umd.edu
Rupert FreemanMicrosoft ResearchVerified email at cs.duke.edu
Jinsong TanUber TechnologiesVerified email at uber.com
Yishay MansourTel Aviv UniversityVerified email at tau.ac.il

Jennifer Wortman Vaughan

Senior Principal Researcher, Microsoft Research, New York City

Verified email at microsoft.com - Homepage

FATE Machine Learning Algorithmic Economics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A theory of learning from different domains S Ben-David, J Blitzer, K Crammer, A Kulesza, F Pereira, JW Vaughan Machine Learning 79 (1-2), 151-175, 2010	1490	2010
Learning bounds for domain adaptation J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman Advances in neural information processing systems, 129-136, 2007	384	2007
Online task assignment in crowdsourcing markets CJ Ho, JW Vaughan Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012	343	2012
Adaptive task assignment for crowdsourced classification CJ Ho, S Jabbari, JW Vaughan Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013	286	2013
Datasheets for Datasets T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, ... arXiv preprint arXiv:1803.09010, 2018	270	2018
Learning from multiple sources K Crammer, M Kearns, J Wortman Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008	221	2008
Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms E Nudelman, J Wortman, Y Shoham, K Leyton-Brown Proceedings of the Third International Joint Conference on Autonomous Agents …, 2004	211	2004
Manipulating and measuring model interpretability F Poursabzi-Sangdeh, DG Goldstein, JM Hofman, JW Vaughan, ... arXiv preprint arXiv:1802.07810, 2018	183	2018
The true sample complexity of active learning MF Balcan, S Hanneke, JW Vaughan Machine learning 80 (2-3), 111-139, 2010	183	2010
The true sample complexity of active learning M Balcan, S Hanneke, J Wortman Twenty-First Annual Conference on Learning Theory, 2008	183	2008
Improving fairness in machine learning systems: What do industry practitioners need? K Holstein, J Wortman Vaughan, H Daumé III, M Dudik, H Wallach Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems …, 2019	159	2019
Behavioral experiments on biased voting in networks M Kearns, S Judd, J Tan, J Wortman Proceedings of the National Academy of Sciences 106 (5), 1347-1352, 2009	157	2009
Exploration scavenging J Langford, A Strehl, J Wortman Proceedings of the 25th international conference on Machine learning, 528-535, 2008	137	2008
Incentivizing High Quality Crowdwork CJ Ho, A Slivkins, S Suri, JW Vaughan Proceedings of the 24th International Conference on World Wide Web, 419-429, 2015	111	2015
A new understanding of prediction markets via no-regret learning Y Chen, JW Vaughan Proceedings of the 11th ACM conference on Electronic commerce, 189-198, 2010	106	2010
Efficient market making via convex optimization, and a connection to online learning J Abernethy, Y Chen, JW Vaughan ACM Transactions on Economics and Computation 1 (2), 12, 2013	82	2013
Online decision making in crowdsourcing markets: Theoretical challenges A Slivkins, JW Vaughan ACM SIGecom Exchanges 12 (2), 4-23, 2014	80	2014
The Communication Network Within the Crowd M Yin, ML Gray, S Suri, JW Vaughan Proceedings of the 25th International Conference on World Wide Web, 1293-1303, 2016	71	2016
Censored exploration and the dark pool problem K Ganchev, Y Nevmyvaka, M Kearns, JW Vaughan Communications of the ACM 53 (5), 99-107, 2010	70	2010
Censored exploration and the dark pool problem K Ganchev, M Kearns, Y Nevmyvaka, JW Vaughan Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial …, 2009	70	2009

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