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pro vyhledávání: '"Anastasios N. Angelopoulos"'
Publikováno v:
Foundations and Trends® in Machine Learning. 16:494-591
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::422a1902ee827ced0da47118d696b28a
https://doi.org/10.1561/2200000101
https://doi.org/10.1561/2200000101
Autor:
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, Michael I. Jordan
Publikováno v:
Proceedings of the National Academy of Sciences. 119
Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what data to consider next. For example, a data-driven approach for designi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbad2c9091ae46bb9ab4a08796e948d5
https://doi.org/10.1073/pnas.2204569119
https://doi.org/10.1073/pnas.2204569119
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164514
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1b3b08179dfb83d4ba1fa81cf1194541
https://doi.org/10.1007/978-3-031-16452-1_52
https://doi.org/10.1007/978-3-031-16452-1_52
Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating sta