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Found 4 papers, 3 papers with code
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks.
Expectation consistency for calibration of neural networks
Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence.
On double-descent in uncertainty quantification in overparametrized models
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning.
Theoretical characterization of uncertainty in high-dimensional linear classification
In this manuscript, we characterise uncertainty for learning from limited number of samples of high-dimensional Gaussian input data and labels generated by the probit model.
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