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Lyle Ungar

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

May 03, 2022
Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser

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* Accepted to NAACL 2022

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Prospective Learning: Back to the Future

Jan 19, 2022
Joshua T. Vogelstein, Timothy Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal Burns, Kwame Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena Savonenko, Ian Phillips, Michael I. Miller, Rene Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish Thakor, Justus M Kebschull, Marilyn Albert, Jinchong Xu, Marshall Hussain Shuler, Brian Caffo, Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan, Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael Platt, Lyle Ungar, Leila Wehbe, Adam Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang

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Social Media Reveals Urban-Rural Differences in Stress across China

Nov 03, 2021
Jesse Cui, Tingdan Zhang, Kokil Jaidka, Dandan Pang, Garrick Sherman, Vinit Jakhetiya, Lyle Ungar, Sharath Chandra Guntuku

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* Accepted at AAAI Conference on Web and Social Media (ICWSM) 2022

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Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica

Sep 06, 2021
Shirley Anugrah Hayati, Dongyeop Kang, Lyle Ungar

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* Accepted at EMNLP 2021 Main Conference

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Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments

Oct 10, 2020
Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Lyle Ungar

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* Accepted in EMNLP 2020

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Generalized SHAP: Generating multiple types of explanations in machine learning

Jun 15, 2020
Dillon Bowen, Lyle Ungar

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* 12 pages, 7 figures. Based on a submission to NeurIPS 2020. Dillon Bowen is credited with the original concept, code, data analysis, and initial paper draft. Lyle Ungar is credited with contributions to the draft and mathematical notation. Documentation can be found at https://dsbowen.github.io/gshap/

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