Jennifer Wortman Vaughan
Jennifer Wortman Vaughan
Senior Principal Researcher, Microsoft Research, New York City
Verified email at - Homepage
Cited by
Cited by
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
Learning bounds for domain adaptation
J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman
Advances in neural information processing systems, 129-136, 2007
Online task assignment in crowdsourcing markets
CJ Ho, JW Vaughan
Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012
Adaptive task assignment for crowdsourced classification
CJ Ho, S Jabbari, JW Vaughan
Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013
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
Learning from multiple sources
K Crammer, M Kearns, J Wortman
Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008
The true sample complexity of active learning
MF Balcan, S Hanneke, JW Vaughan
Machine learning 80 (2-3), 111-139, 2010
The true sample complexity of active learning
M Balcan, S Hanneke, J Wortman
Twenty-First Annual Conference on Learning Theory, 2008
Datasheets for Datasets
T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, ...
arXiv preprint arXiv:1803.09010, 2018
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
Manipulating and measuring model interpretability
F Poursabzi-Sangdeh, DG Goldstein, JM Hofman, JW Vaughan, ...
arXiv preprint arXiv:1802.07810, 2018
Exploration scavenging
J Langford, A Strehl, J Wortman
Proceedings of the 25th international conference on Machine learning, 528-535, 2008
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
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
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
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
Online decision making in crowdsourcing markets: Theoretical challenges
A Slivkins, JW Vaughan
ACM SIGecom Exchanges 12 (2), 4-23, 2014
Censored exploration and the dark pool problem
K Ganchev, Y Nevmyvaka, M Kearns, JW Vaughan
Communications of the ACM 53 (5), 99-107, 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
Complexity of combinatorial market makers
Y Chen, L Fortnow, N Lambert, DM Pennock, J Wortman
Proceedings of the 9th ACM Conference on Electronic Commerce, 190-199, 2008
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