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Jennifer Wortman Vaughan
Jennifer Wortman Vaughan
Senior Principal Researcher, Microsoft Research, New York City
E-mail confirmado em microsoft.com - Página inicial
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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
22512010
Datasheets for datasets
T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, ...
arXiv preprint arXiv:1803.09010, 2018
7332018
Learning bounds for domain adaptation
J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman
Advances in neural information processing systems, 129-136, 2007
4532007
Online task assignment in crowdsourcing markets
CJ Ho, JW Vaughan
Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012
3852012
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
3592019
Manipulating and measuring model interpretability
F Poursabzi-Sangdeh, DG Goldstein, JM Hofman, JW Wortman Vaughan, ...
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
3562021
Adaptive task assignment for crowdsourced classification
CJ Ho, S Jabbari, JW Vaughan
Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013
3122013
Learning from multiple sources
K Crammer, M Kearns, J Wortman
Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008
2712008
Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms
E Nudelman, J Wortman, Y Shoham, K Leyton-Brown
AAMAS 4, 880-887, 2004
2302004
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning
H Kaur, H Nori, S Jenkins, R Caruana, H Wallach, J Wortman Vaughan
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …, 2020
2032020
The true sample complexity of active learning
MF Balcan, S Hanneke, JW Vaughan
Machine learning 80 (2-3), 111-139, 2010
1852010
The true sample complexity of active learning
M Balcan, S Hanneke, J Wortman
Twenty-First Annual Conference on Learning Theory, 2008
1852008
Understanding the Effect of Accuracy on Trust in Machine Learning Models
M Yin, J Wortman Vaughan, H Wallach
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems …, 2019
1842019
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
1732009
Exploration scavenging
J Langford, A Strehl, J Wortman
Proceedings of the 25th international conference on Machine learning, 528-535, 2008
1442008
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
1422015
Co-designing checklists to understand organizational challenges and opportunities around fairness in ai
MA Madaio, L Stark, J Wortman Vaughan, H Wallach
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …, 2020
1362020
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
1112010
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research.
JW Vaughan
Journal of Machine Learning Research 18, 193:1-193:46, 2017
1102017
The Disparate Effects of Strategic Manipulation
L Hu, N Immorlica, JW Vaughan
ACM Conference on Fairness, Accountability, and Transparency (FAT*), 2019
1032019
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