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ORCIDHanna M. Wallach
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- affiliation: Microsoft Research, New York, NY, USA
- affiliation: University of Massachusetts Amherst, USA
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Journal Articles
- 2022
[j10]Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, Hanna M. Wallach:
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support. Proc. ACM Hum. Comput. Interact. 6(CSCW1): 52:1-52:26 (2022)- [j9]Amy Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna M. Wallach, Jennifer Wortman Vaughan:
Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata. Proc. ACM Hum. Comput. Interact. 6(CSCW2): 1-29 (2022) - 2021
- [j8]Solon Barocas
, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miroslav Dudík, Benjamin Fish, Mary L. Gray, Brent J. Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna M. Wallach, Marion Zepf:
Responsible computing during COVID-19 and beyond. Commun. ACM 64(7): 30-32 (2021) - [j7]Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford:
Datasheets for datasets. Commun. ACM 64(12): 86-92 (2021) - [j6]Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan:
Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. 44(4): 14-27 (2021) - [j5]Adina Williams, Lawrence Wolf-Sonkin, Damián E. Blasi, Hanna M. Wallach, Ryan Cotterell:
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs. Trans. Assoc. Comput. Linguistics 9: 139-159 (2021) - 2020
- [j4]Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna M. Wallach, Meredith Ringel Morris:
Toward fairness in AI for people with disabilities SBG@a research roadmap. ACM SIGACCESS Access. Comput. 125: 2 (2020) - 2018
- [j3]Hanna M. Wallach:
Computational social science ≠ computer science + social data. Commun. ACM 61(3): 42-44 (2018) - 2017
- [j2]Solon Barocas, danah boyd, Sorelle A. Friedler, Hanna M. Wallach:
Social and Technical Trade-Offs in Data Science. Big Data 5(2): 71-72 (2017) - 2014
- [j1]Winter A. Mason, Jennifer Wortman Vaughan, Hanna M. Wallach:
Computational social science and social computing. Mach. Learn. 95(3): 257-260 (2014)
Conference and Workshop Papers
- 2023
- [c50]Jared Katzman, Angelina Wang, Morgan Klaus Scheuerman, Su Lin Blodgett, Kristen Laird, Hanna M. Wallach, Solon Barocas:
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging. AAAI 2023: 14277-14285 - [c49]Eve Fleisig, Aubrie Amstutz, Chad Atalla, Su Lin Blodgett, Hal Daumé III, Alexandra Olteanu, Emily Sheng, Dan Vann, Hanna M. Wallach:
FairPrism: Evaluating Fairness-Related Harms in Text Generation. ACL (1) 2023: 6231-6251 - [c48]Daricia Wilkinson, Kate Crawford, Hanna M. Wallach, Deborah Raji, Bogdana Rakova, Ranjit Singh, Angelika Strohmayer, Ethan Zuckerman:
Accountability in Algorithmic Systems: From Principles to Practice. CHI Extended Abstracts 2023: 521:1-521:4 - 2022
- [c47]Angelina Wang, Solon Barocas, Kristen Laird, Hanna M. Wallach:
Measuring Representational Harms in Image Captioning. FAccT 2022: 324-335 - [c46]Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna M. Wallach, Jennifer Wortman Vaughan:
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. FAccT 2022: 587-597 - 2021
- [c45]Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, Hanna M. Wallach:
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets. ACL/IJCNLP (1) 2021: 1004-1015 - [c44]Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, W. Duncan Wadsworth, Hanna M. Wallach:
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs. AIES 2021: 368-378 - [c43]Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach:
Manipulating and Measuring Model Interpretability. CHI 2021: 237:1-237:52 - [c42]David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna M. Wallach, Jennifer Wortman Vaughan:
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence. HCOMP 2021: 35-47 - [c41]Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan:
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. DaSH@KDD 2021 - [c40]Aaron Schein, Anjali Nagulpally, Hanna M. Wallach, Patrick Flaherty:
Doubly non-central beta matrix factorization for DNA methylation data. UAI 2021: 1895-1904 - 2020
- [c39]Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach:
Language (Technology) is Power: A Critical Survey of "Bias" in NLP. ACL 2020: 5454-5476 - [c38]Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan:
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. CHI 2020: 1-14 - [c37]Michael A. Madaio, Luke Stark, Jennifer Wortman Vaughan, Hanna M. Wallach:
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. CHI 2020: 1-14 - [c36]Kathy Baxter, Yoav Schlesinger, Sarah Aerni, Lewis J. Baker, Julie Dawson, Krishnaram Kenthapadi, Isabel M. Kloumann, Hanna M. Wallach:
Bridging the gap from AI ethics research to practice. FAT* 2020: 682 - [c35]Abigail Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach:
The meaning and measurement of bias: lessons from natural language processing. FAT* 2020: 706 - 2019
- [c34]Ran Zmigrod, S. J. Mielke, Hanna M. Wallach, Ryan Cotterell:
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology. ACL (1) 2019: 1651-1661 - [c33]Alexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna M. Wallach, Isabelle Augenstein, Ryan Cotterell:
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. ACL (1) 2019: 1706-1716 - [c32]Ming Yin, Jennifer Wortman Vaughan, Hanna M. Wallach:
Understanding the Effect of Accuracy on Trust in Machine Learning Models. CHI 2019: 279 - [c31]Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna M. Wallach:
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? CHI 2019: 600 - [c30]Adina Williams, Damián E. Blasi, Lawrence Wolf-Sonkin, Hanna M. Wallach, Ryan Cotterell:
Quantifying the Semantic Core of Gender Systems. EMNLP/IJCNLP (1) 2019: 5733-5738 - [c29]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai:
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. FAT 2019: 120-128 - [c28]Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna M. Wallach:
Locally Private Bayesian Inference for Count Models. ICML 2019: 5638-5648 - [c27]Alexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna M. Wallach, Ryan Cotterell, Isabelle Augenstein:
Combining Sentiment Lexica with a Multi-View Variational Autoencoder. NAACL-HLT (1) 2019: 635-640 - [c26]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai:
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. NAACL-HLT (1) 2019: 4187-4195 - [c25]Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Poisson-Randomized Gamma Dynamical Systems. NeurIPS 2019: 781-792 - 2018
- [c24]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. ICML 2018: 60-69 - 2017
- [c23]Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna M. Wallach, Emine Yilmaz:
Auditing Search Engines for Differential Satisfaction Across Demographics. WWW (Companion Volume) 2017: 626-633 - 2016
- [c22]Abram Handler, Matthew Denny, Hanna M. Wallach, Brendan O'Connor:
Bag of What? Simple Noun Phrase Extraction for Text Analysis. NLP+CSS@EMNLP 2016: 114-124 - [c21]Allison June-Barlow Chaney, Hanna M. Wallach, Matthew Connelly, David M. Blei:
Detecting and Characterizing Events. EMNLP 2016: 1142-1152 - [c20]Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. ICML 2016: 2810-2819 - [c19]Brenda Betancourt, Giacomo Zanella, Jeffrey W. Miller, Hanna M. Wallach, Abbas Zaidi, Beka Steorts:
Flexible Models for Microclustering with Application to Entity Resolution. NIPS 2016: 1417-1425 - [c18]Aaron Schein, Hanna M. Wallach, Mingyuan Zhou:
Poisson-Gamma dynamical systems. NIPS 2016: 5006-5014 - [c17]Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna M. Wallach, Fernando Diaz, Jacob Eisenstein:
The Social Dynamics of Language Change in Online Networks. SocInfo (1) 2016: 41-57 - 2015
- [c16]Fangjian Guo, Charles Blundell, Hanna M. Wallach, Katherine A. Heller:
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation. AISTATS 2015 - [c15]Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. KDD 2015: 1045-1054 - 2014
- [c14]Scott Counts, Munmun De Choudhury, Jana Diesner, Eric Gilbert, Marta C. González, Brian Keegan, Mor Naaman, Hanna M. Wallach:
Computational social science: CSCW in the social media era. CSCW Companion 2014: 105-108 - 2013
- [c13]Kriste Krstovski, David A. Smith, Hanna M. Wallach, Andrew McGregor:
Efficient Nearest-Neighbor Search in the Probability Simplex. ICTIR 2013: 22 - 2012
- [c12]Anton Bakalov, Andrew McCallum, Hanna M. Wallach, David M. Mimno:
Topic models for taxonomies. JCDL 2012: 237-240 - [c11]Peter M. Krafft, Juston Moore, Bruce A. Desmarais, Hanna M. Wallach:
Topic-Partitioned Multinetwork Embeddings. NIPS 2012: 2816-2824 - 2011
- [c10]David M. Mimno, Hanna M. Wallach, Edmund M. Talley, Miriam Leenders, Andrew McCallum:
Optimizing Semantic Coherence in Topic Models. EMNLP 2011: 262-272 - 2010
- [c9]Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghahramani:
Learning the Structure of Deep Sparse Graphical Models. AISTATS 2010: 1-8 - [c8]Hanna M. Wallach, Shane T. Jensen, Lee H. Dicker, Katherine A. Heller:
An Alternative Prior Process for Nonparametric Bayesian Clustering. AISTATS 2010: 892-899 - 2009
- [c7]David M. Mimno, Hanna M. Wallach, Jason Naradowsky, David A. Smith, Andrew McCallum:
Polylingual Topic Models. EMNLP 2009: 880-889 - [c6]Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, David M. Mimno:
Evaluation methods for topic models. ICML 2009: 1105-1112 - [c5]Hanna M. Wallach, David M. Mimno, Andrew McCallum:
Rethinking LDA: Why Priors Matter. NIPS 2009: 1973-1981 - 2008
- [c4]Mark Dredze, Hanna M. Wallach, Danny Puller, Tova Brooks, Josh Carroll, Joshua Magarick, John Blitzer, Fernando Pereira:
Intelligent Email: Aiding Users with AI. AAAI 2008: 1524-1527 - [c3]Mark Dredze, Hanna M. Wallach, Danny Puller, Fernando Pereira:
Generating summary keywords for emails using topics. IUI 2008: 199-206 - 2006
- [c2]Hanna M. Wallach:
Topic modeling: beyond bag-of-words. ICML 2006: 977-984 - 2002
- [c1]Alan F. Blackwell, Hanna M. Wallach:
Diagrammatic Integration of Abstract Operations into Software Work Contexts. Diagrams 2002: 191-205
Parts in Books or Collections
- 2016
- [p1]Hanna M. Wallach:
Conclusion - Computational Social Science: Toward a Collaborative Future. Computational Social Science 2016: 307-316
Editorship
- 2019
- [e4]Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett:
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 2019 [contents] - 2018
- [e3]Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, Roman Garnett:
Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada. 2018 [contents] - 2017
- [e2]Dirk Hovy, Shannon L. Spruit, Margaret Mitchell, Emily M. Bender, Michael Strube, Hanna M. Wallach:
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, EthNLP@EACL, Valencia, Spain, April 4, 2017. Association for Computational Linguistics 2017, ISBN 978-1-945626-47-0 [contents] - [e1]Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett:
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 2017 [contents]
Informal and Other Publications
- 2023
- [i34]Jared Katzman, Angelina Wang, Morgan Klaus Scheuerman, Su Lin Blodgett, Kristen Laird, Hanna M. Wallach, Solon Barocas:
Taxonomizing and Measuring Representational Harms: A Look at Image Tagging. CoRR abs/2305.01776 (2023) - [i33]Li Lucy, Su Lin Blodgett, Milad Shokouhi, Hanna M. Wallach, Alexandra Olteanu:
"One-size-fits-all"? Observations and Expectations of NLG Systems Across Identity-Related Language Features. CoRR abs/2310.15398 (2023) - [i32]Ahmed Magooda, Alec Helyar, Kyle Jackson, David Sullivan, Chad Atalla, Emily Sheng, Dan Vann, Richard Edgar, Hamid Palangi, Roman Lutz, Hongliang Kong, Vincent Yun, Eslam Kamal, Federico Zarfati, Hanna M. Wallach, Sarah Bird, Mei Chen:
A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications. CoRR abs/2310.17750 (2023) - 2022
- [i31]Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna M. Wallach, Jennifer Wortman Vaughan:
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. CoRR abs/2205.08363 (2022) - [i30]Amy Heger, Elizabeth B. Marquis, Mihaela Vorvoreanu, Hanna M. Wallach, Jennifer Wortman Vaughan:
Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata. CoRR abs/2206.02923 (2022) - [i29]Angelina Wang, Solon Barocas, Kristen Laird, Hanna M. Wallach:
Measuring Representational Harms in Image Captioning. CoRR abs/2206.07173 (2022) - 2021
- [i28]Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, W. Duncan Wadsworth, Hanna M. Wallach:
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs. CoRR abs/2103.06076 (2021) - [i27]David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna M. Wallach, Jennifer Wortman Vaughan:
A Human-Centered Interpretability Framework Based on Weight of Evidence. CoRR abs/2104.13299 (2021) - [i26]Aaron Schein, Anjali Nagulpally, Hanna M. Wallach, Patrick Flaherty:
Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data. CoRR abs/2106.06691 (2021) - [i25]Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, Hanna M. Wallach:
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support. CoRR abs/2112.05675 (2021) - 2020
- [i24]Adina Williams, Ryan Cotterell, Lawrence Wolf-Sonkin, Damián E. Blasi, Hanna M. Wallach:
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs. CoRR abs/2005.01204 (2020) - [i23]Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach:
Language (Technology) is Power: A Critical Survey of "Bias" in NLP. CoRR abs/2005.14050 (2020) - 2019
- [i22]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai:
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. CoRR abs/1901.09451 (2019) - [i21]Alexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna M. Wallach, Ryan Cotterell, Isabelle Augenstein:
Combining Sentiment Lexica with a Multi-View Variational Autoencoder. CoRR abs/1904.02839 (2019) - [i20]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Tauman Kalai:
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. CoRR abs/1904.05233 (2019) - [i19]Ran Zmigrod, Sabrina J. Mielke, Hanna M. Wallach, Ryan Cotterell:
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology. CoRR abs/1906.04571 (2019) - [i18]Alexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna M. Wallach, Isabelle Augenstein, Ryan Cotterell:
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling. CoRR abs/1906.04760 (2019) - [i17]Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna M. Wallach, Meredith Ringel Morris:
Toward Fairness in AI for People with Disabilities: A Research Roadmap. CoRR abs/1907.02227 (2019) - [i16]Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Poisson-Randomized Gamma Dynamical Systems. CoRR abs/1910.12991 (2019) - [i15]Adina Williams, Ryan Cotterell, Lawrence Wolf-Sonkin, Damián E. Blasi, Hanna M. Wallach:
Quantifying the Semantic Core of Gender Systems. CoRR abs/1910.13497 (2019) - [i14]David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, Hanna M. Wallach:
Weight of Evidence as a Basis for Human-Oriented Explanations. CoRR abs/1910.13503 (2019) - [i13]Abigail Z. Jacobs, Hanna M. Wallach:
Measurement and Fairness. CoRR abs/1912.05511 (2019) - 2018
- [i12]Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach:
Manipulating and Measuring Model Interpretability. CoRR abs/1802.07810 (2018) - [i11]Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, Hanna M. Wallach:
A Reductions Approach to Fair Classification. CoRR abs/1803.02453 (2018) - [i10]Aaron Schein, Zhiwei Steven Wu, Mingyuan Zhou, Hanna M. Wallach:
Locally Private Bayesian Inference for Count Models. CoRR abs/1803.08471 (2018) - [i9]Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford:
Datasheets for Datasets. CoRR abs/1803.09010 (2018) - [i8]Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna M. Wallach:
Improving fairness in machine learning systems: What do industry practitioners need? CoRR abs/1812.05239 (2018) - 2017
- [i7]Aaron Schein, Mingyuan Zhou, Hanna M. Wallach:
Poisson-Gamma Dynamical Systems. CoRR abs/1701.05573 (2017) - [i6]Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna M. Wallach, Emine Yilmaz:
Auditing Search Engines for Differential Satisfaction Across Demographics. CoRR abs/1705.10689 (2017) - 2016
- [i5]Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. CoRR abs/1606.01855 (2016) - [i4]Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna M. Wallach, Fernando Diaz, Jacob Eisenstein:
The Social Dynamics of Language Change in Online Networks. CoRR abs/1609.02075 (2016) - 2015
- [i3]Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. CoRR abs/1506.03493 (2015) - 2014
- [i2]Fangjian Guo, Charles Blundell, Hanna M. Wallach, Katherine A. Heller:
The Bayesian Echo Chamber: Modeling Influence in Conversations. CoRR abs/1411.2674 (2014) - 2013
- [i1]Aaron Schein, Juston Moore, Hanna M. Wallach:
Inferring Multilateral Relations from Dynamic Pairwise Interactions. CoRR abs/1311.3982 (2013)
Coauthor Index
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