| Optimizing semantic coherence in topic models D Mimno, H Wallach, E Talley, M Leenders, A McCallum Proceedings of the 2011 conference on empirical methods in natural language …, 2011 | 2735 | 2011 |
| Datasheets for datasets T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, HD Iii, ... Communications of the ACM 64 (12), 86-92, 2021 | 2663 | 2021 |
| Topic modeling: beyond bag-of-words HM Wallach Proceedings of the 23rd international conference on Machine learning, 977-984, 2006 | 1720 | 2006 |
| Advances in neural information processing systems 31 S Bengio, HM Wallach, H Larochelle, K Grauman, N Cesa‐Bianchi, ... Annual conference on neural information processing systems 2018, NeurIPS …, 2018 | 1474* | 2018 |
| Language (Technology) is Power: A Critical Survey of "Bias" in NLP SL Blodgett, S Barocas, H Daumé III, H Wallach arXiv preprint arXiv:2005.14050, 2020 | 1342 | 2020 |
| A reductions approach to fair classification A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach International conference on machine learning, 60-69, 2018 | 1337 | 2018 |
| Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009 | 1312 | 2009 |
| Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009 | 1030 | 2009 |
| 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 | 965 | 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 | 897 | 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 | 632 | 2020 |
| Conditional random fields: An introduction HM Wallach Technical reports (CIS) 22, 2-6, 2004 | 622 | 2004 |
| 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 | 610 | 2019 |
| Bias in bios: A case study of semantic representation bias in a high-stakes setting M De-Arteaga, A Romanov, H Wallach, J Chayes, C Borgs, ... proceedings of the Conference on Fairness, Accountability, and Transparency …, 2019 | 532 | 2019 |
| Polylingual topic models D Mimno, H Wallach, J Naradowsky, DA Smith, A McCallum Proceedings of the 2009 conference on empirical methods in natural language …, 2009 | 489 | 2009 |
| 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 | 485 | 2020 |
| Fairlearn: A toolkit for assessing and improving fairness in AI S Bird, M Dudík, R Edgar, B Horn, R Lutz, V Milan, M Sameki, H Wallach, ... Microsoft, Tech. Rep. MSR-TR-2020-32, 2020 | 478 | 2020 |
| Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, 2019 HM Wallach, H Larochelle, A Beygelzimer, F d’Alché-Buc, EB Fox, ... URL http://papers. nips. cc/book/advances-in-neural-information-processing …, 2019 | 465* | 2019 |
| Measurement and fairness AZ Jacobs, H Wallach Proceedings of the 2021 ACM conference on fairness, accountability, and …, 2021 | 460 | 2021 |
| Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology R Zmigrod, SJ Mielke, H Wallach, R Cotterell arXiv preprint arXiv:1906.04571, 2019 | 331 | 2019 |
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York City在 microsoft.com 的电子邮件经过验证