Bootstrapped Loss Estimation
Bootstrapped Loss Estimation is a statistical method for quantifying uncertainty in loss predictions by repeatedly resampling observed loss data with replacement to generate empirical distributions of estimated loss parameters.
5
Publications
391
Citations
13
Authors
6
Institutions
Publications per year
2017–2026
1
Parents
5
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert et al. · arXiv (Cornell University) · 2019 · 198 citations · Full text
An overview of bootstrap methods for estimating and predicting in time series
Ricardo Cao · Test · 1999 · 27 citations
1–5 of 5
13
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IBM Research - Thomas J. Watson Research Center, United States
1 publications · 86 citations
1
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1
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1
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1
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1–5 of 13
6

Universidad Autónoma de Madrid
Madrid, Spain
1 h-index
1 publications
198 citations
IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
1 h-index
3 publications
258 citations
IBM (United States)
Armonk, United States
1 h-index
3 publications
258 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
1 h-index
1 publications
33 citations

Universidade da Coruña
A Coruña, Spain
1 h-index
1 publications
27 citations
Dublin City University
Dublin, Ireland
1 h-index
4 publications
792 citations
1–6 of 6
CVIU · 1 publications · 86 citations
1
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Repository · 1 publications · 33 citations
1
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1 publications · 47 citations
1
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1 publications · 27 citations
1
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Repository · 1 publications · 198 citations
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