Hanna Wallach

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Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
David MimnoAssociate Professor, Cornell UniversityVerified email at cornell.edu
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandVerified email at umiacs.umd.edu
Solon BarocasPrincipal Researcher, Microsoft Research; Adjunct Assistant Professor, Information Science, CornellVerified email at cornell.edu
Aaron ScheinPostdoctoral Fellow in Data Science Institute at Columbia UniversityVerified email at columbia.edu
Miroslav DudikMicrosoft ResearchVerified email at microsoft.com
Briana VecchioneCornell UniversityVerified email at cornell.edu
Su Lin BlodgettMicrosoft Research MontréalVerified email at microsoft.com
Edmund M TalleyNational Institutes of HealthVerified email at nih.gov
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Bruce A. DesmaraisProfessor, Department of Political Science, Penn State UniversityVerified email at psu.edu
Jamie MorgensternUniversity of WashingtonVerified email at cs.washington.edu
Mingyuan ZhouAssociate Professor, University of Texas at AustinVerified email at mccombs.utexas.edu
Ryan CotterellETH ZürichVerified email at inf.ethz.ch
Michael A. MadaioGoogle ResearchVerified email at google.com
Katherine HellerGoogle ResearchVerified email at gatsby.ucl.ac.uk
Kenneth HolsteinCarnegie Mellon UniversityVerified email at cs.cmu.edu
Iain MurraySchool of Informatics, University of Edinburgh and AmazonVerified email at ed.ac.uk
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu

Hanna Wallach

Partner Research Manager, Microsoft Research

Verified email at dirichlet.net - Homepage

Fairness Interpretability Computational Social Science Machine Learning Natural Language Processing


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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	1950	2011
Topic modeling: beyond bag-of-words HM Wallach Proceedings of the 23rd international conference on Machine learning, 977-984, 2006	1524	2006
Datasheets for datasets T Gebru, J Morgenstern, B Vecchione, JW Vaughan, H Wallach, HD Iii, ... Communications of the ACM 64 (12), 86-92, 2021	1162	2021
Evaluation methods for topic models HM Wallach, I Murray, R Salakhutdinov, D Mimno Proceedings of the 26th annual international conference on machine learning …, 2009	1097	2009
Rethinking LDA: Why priors matter H Wallach, D Mimno, A McCallum Advances in neural information processing systems 22, 2009	894	2009
A reductions approach to fair classification A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach International Conference on Machine Learning, 60-69, 2018	759	2018
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	749*	2018
Conditional random fields: An introduction HM Wallach Technical Reports (CIS), 22, 2004	558	2004
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	550	2020
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	509	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	493	2021
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	443	2009
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	292	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	290*	2019
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	287	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	246	2019
Efficient training of conditional random fields H Wallach Master’s thesis, University of Edinburgh, 2002	232	2002
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	225	2020
Structured topic models for language HM Wallach University of Cambridge, 2008	220	2008
Measurement and fairness AZ Jacobs, H Wallach Proceedings of the 2021 ACM conference on fairness, accountability, and …, 2021	189	2021

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