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pro vyhledávání: '"Zhou, Joyce"'
Many online platforms maintain user profiles for personalization. Unfortunately, these profiles are typically not interpretable or easily modifiable by the user. To remedy this shortcoming, we explore natural language-based user profiles, as they pro
Externí odkaz:
http://arxiv.org/abs/2410.18870
Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented challenges. Fi
Externí odkaz:
http://arxiv.org/abs/2407.11199
Most conventional recommendation methods (e.g., matrix factorization) represent user profiles as high-dimensional vectors. Unfortunately, these vectors lack interpretability and steerability, and often perform poorly in cold-start settings. To addres
Externí odkaz:
http://arxiv.org/abs/2402.15623
Autor:
Zhou, Joyce, Joachims, Thorsten
In this work, we establish a baseline potential for how modern model-generated text explanations of movie recommendations may help users, and explore what different components of these text explanations that users like or dislike, especially in contr
Externí odkaz:
http://arxiv.org/abs/2309.08817
University admission at many highly selective institutions uses a holistic review process, where all aspects of the application, including protected attributes (e.g., race, gender), grades, essays, and recommendation letters are considered, to compos
Externí odkaz:
http://arxiv.org/abs/2306.17575
Scientists and science journalists, among others, often need to make sense of a large number of papers and how they compare with each other in scope, focus, findings, or any other important factors. However, with a large corpus of papers, it's cognit
Externí odkaz:
http://arxiv.org/abs/2303.06264
Autor:
Choi, Jaewoo, Ceribelli, Michele, Phelan, James D., Häupl, Björn, Huang, Da Wei, Wright, George W., Hsiao, Tony, Morris, Vivian, Ciccarese, Francesco, Wang, Boya, Corcoran, Sean, Scheich, Sebastian, Yu, Xin, Xu, Weihong, Yang, Yandan, Zhao, Hong, Zhou, Joyce, Zhang, Grace, Muppidi, Jagan, Inghirami, Giorgio G., Oellerich, Thomas, Wilson, Wyndham H., Thomas, Craig J., Staudt, Louis M.
Publikováno v:
In Cancer Cell 13 May 2024 42(5):833-849
Autor:
Bansal, Gagan, Wu, Tongshuang, Zhou, Joyce, Fok, Raymond, Nushi, Besmira, Kamar, Ece, Ribeiro, Marco Tulio, Weld, Daniel S.
Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, prior studies observed improvements from explanations only when the AI,
Externí odkaz:
http://arxiv.org/abs/2006.14779
Autor:
Zhou, Joyce C., Sise, Meghan E., Drezek, Kamila, Wolfe, Stanley B., Osho, Asishana A., Prario, Monica N., Rabi, S. Alireza, Michel, Eriberto, Tsao, Lana, Coglianese, Erin, Doucette, Meaghan, Newton-Cheh, Christopher, Thomas, Sunu, Van-Khue Ton, Sutaria, Nilay, Schoenike, Mark W., Christ, Anastasia M., Paneitz, Dane C., Villavicencio, Mauricio, Madsen, Joren C.
Publikováno v:
Journal of the American Heart Association; 10/15/2024, Vol. 13 Issue 20, p1-11, 11p
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