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pro vyhledávání: '"Hofman, Jake M."'
Autor:
Kapoor, Sayash, Cantrell, Emily, Peng, Kenny, Pham, Thanh Hien, Bail, Christopher A., Gundersen, Odd Erik, Hofman, Jake M., Hullman, Jessica, Lones, Michael A., Malik, Momin M., Nanayakkara, Priyanka, Poldrack, Russell A., Raji, Inioluwa Deborah, Roberts, Michael, Salganik, Matthew J., Serra-Garcia, Marta, Stewart, Brandon M., Vandewiele, Gilles, Narayanan, Arvind
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to
Externí odkaz:
http://arxiv.org/abs/2308.07832
Numerical perspectives help people understand extreme and unfamiliar numbers (e.g., \$330 billion is about \$1,000 per person in the United States). While research shows perspectives to be helpful, generating them at scale is challenging both because
Externí odkaz:
http://arxiv.org/abs/2308.01535
Recent advances in the development of large language models are rapidly changing how online applications function. LLM-based search tools, for instance, offer a natural language interface that can accommodate complex queries and provide detailed, dir
Externí odkaz:
http://arxiv.org/abs/2307.03744
Publikováno v:
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, June 2023, Pages 297-311
Prediction models have been widely adopted as the basis for decision-making in domains as diverse as employment, education, lending, and health. Yet, few real world problems readily present themselves as precisely formulated prediction tasks. In part
Externí odkaz:
http://arxiv.org/abs/2306.13738
Autor:
Poursabzi-Sangdeh, Forough, Goldstein, Daniel G., Hofman, Jake M., Vaughan, Jennifer Wortman, Wallach, Hanna
With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed, there hav
Externí odkaz:
http://arxiv.org/abs/1802.07810
Publikováno v:
Harvard Business Review Digital Articles. 12/4/2023, p1-7. 7p.
Akademický článek
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We present a method for estimating causal effects in time series data when fine-grained information about the outcome of interest is available. Specifically, we examine what we call the split-door setting, where the outcome variable can be split into
Externí odkaz:
http://arxiv.org/abs/1611.09414
How predictable is success in complex social systems? In spite of a recent profusion of prediction studies that exploit online social and information network data, this question remains unanswered, in part because it has not been adequately specified
Externí odkaz:
http://arxiv.org/abs/1602.01013
Autor:
Hofman, Jake M., Goldstein, Daniel G., Sen, Siddhartha, Poursabzi-Sangdeh, Forough, Allen, Jennifer, Dong, Ling Liang, Fried, Brenda, Gaur, Harpreet, Hoq, Adnan, Mbazor, Emeka, Moreira, Naomi, Muso, Cindy, Rapp, Etta, Terrero, Roymil
Publikováno v:
In Organizational Behavior and Human Decision Processes May 2021 164:192-202