Jennifer Wortman Vaughan

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Hanna WallachVP & Distinguished Scientist, Microsoft ResearchDirección de correo verificada de dirichlet.net
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandDirección de correo verificada de umiacs.umd.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaDirección de correo verificada de cis.upenn.edu
Koby CrammerProfessor of Electrical Engineering, Technion - Israel Institute of TechnologyDirección de correo verificada de ee.technion.ac.il
David M PennockProfessor, Rutgers UniversityDirección de correo verificada de dimacs.rutgers.edu
Yiling ChenHarvard UniversityDirección de correo verificada de seas.harvard.edu
Chien-Ju HoWashington University in St. LouisDirección de correo verificada de wustl.edu
Briana VecchioneCornell UniversityDirección de correo verificada de cornell.edu
Aleksandrs SlivkinsSenior Principal Researcher, Microsoft Research NYCDirección de correo verificada de microsoft.com
Miroslav DudikMicrosoft ResearchDirección de correo verificada de microsoft.com
Jamie MorgensternUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Fernando PereiraVP, GoogleDirección de correo verificada de google.com
Alex KuleszaResearch Scientist, GoogleDirección de correo verificada de google.com
John BlitzerGoogle ResearchDirección de correo verificada de google.com
Q.Vera LiaoMicrosoft Research/University of MichiganDirección de correo verificada de microsoft.com
Rich CaruanaMicrosoft ResearchDirección de correo verificada de microsoft.com
Rupert FreemanUniversity of VirginiaDirección de correo verificada de darden.virginia.edu
Harsha NoriMicrosoft ResearchDirección de correo verificada de microsoft.com
Shai Ben-DavidUniverity of Waterloo, CanadaDirección de correo verificada de uwaterloo.ca
Ming YinPurdue UniversityDirección de correo verificada de purdue.edu

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Jennifer Wortman Vaughan

Senior Principal Researcher, Microsoft Research, New York City

Dirección de correo verificada de microsoft.com - Página principal

AI Transparency AI Fairness Responsible AI Machine Learning Algorithmic Economics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	4197	2010
Datasheets for datasets T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, HD Iii, ... Communications of the ACM 64 (12), 86-92, 2021	2750	2021
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	1002	2019
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	908	2021
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	650	2020
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	617	2019
Learning bounds for domain adaptation J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman Advances in neural information processing systems, 129-136, 2007	610	2007
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	488	2020
Online task assignment in crowdsourcing markets CJ Ho, JW Vaughan Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012	452	2012
Learning from multiple sources K Crammer, M Kearns, J Wortman Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008	347	2008
Adaptive task assignment for crowdsourced classification CJ Ho, S Jabbari, JW Vaughan Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013	345	2013
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	257	2004
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	240	2017
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	224	2009
The true sample complexity of active learning MF Balcan, S Hanneke, JW Vaughan Machine learning 80 (2-3), 111-139, 2010	217	2010
The true sample complexity of active learning M Balcan, S Hanneke, J Wortman Twenty-First Annual Conference on Learning Theory, 2008	217	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	210	2015
The Disparate Effects of Strategic Manipulation L Hu, N Immorlica, JW Vaughan ACM Conference on Fairness, Accountability, and Transparency (FAT*), 2019	201	2019
Adaptive contract design for crowdsourcing markets: Bandit algorithms for repeated principal-agent problems CJ Ho, A Slivkins, JW Vaughan Proceedings of the fifteenth ACM conference on Economics and computation …, 2014	163	2014
Toward fairness in AI for people with disabilities: a research roadmap A Guo, E Kamar, JW Vaughan, H Wallach, MR Morris ACM SIGACCESS Accessibility and Computing, 1-1, 2020	160	2020

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