| 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 | 3214 | 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 | 1605 | 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 | 652 | 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 | 610 | 2021 |
| Learning bounds for domain adaptation J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman Advances in neural information processing systems, 129-136, 2007 | 532 | 2007 |
| Online task assignment in crowdsourcing markets CJ Ho, JW Vaughan Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012 | 429 | 2012 |
| 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 | 395 | 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 | 371 | 2019 |
| Adaptive task assignment for crowdsourced classification CJ Ho, S Jabbari, JW Vaughan Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013 | 332 | 2013 |
| 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 | 304 | 2020 |
| Learning from multiple sources K Crammer, M Kearns, J Wortman Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008 | 304 | 2008 |
| 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 | 245 | 2004 |
| The true sample complexity of active learning MF Balcan, S Hanneke, JW Vaughan Machine learning 80 (2-3), 111-139, 2010 | 202 | 2010 |
| The true sample complexity of active learning M Balcan, S Hanneke, J Wortman Twenty-First Annual Conference on Learning Theory, 2008 | 202 | 2008 |
| 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 | 200 | 2009 |
| 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 | 186 | 2017 |
| 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 | 170 | 2015 |
| The Disparate Effects of Strategic Manipulation L Hu, N Immorlica, JW Vaughan ACM Conference on Fairness, Accountability, and Transparency (FAT*), 2019 | 153 | 2019 |
| Exploration scavenging J Langford, A Strehl, J Wortman Proceedings of the 25th international conference on Machine learning, 528-535, 2008 | 153 | 2008 |
| 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 | 126 | 2016 |
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