arXiv 31 Mar 2021 · Statistics — Machine Learning · publishedJournal of the Royal Statistical Society Series B (Statistical Methodology) (2022) · 1 citations (OpenAlex)
arXiv:2103.17203 · PDF · DOI · OpenAlex · Extracted main text
We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, has strong non-asymptotic coverage properties, and is easy to implement using standard convex programs. Our approach can be viewed as a novel variance interpolation with confidence and further leverages techniques from semi-definite programming and sum-of-squares optimization. Theoretical and numerical performances for the proposed approach for uncertainty quantification are analyzed.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan J. Tibshirani, and La… (2017) Distribution-Free Predictive Inference for Regression | 0.811 | 4 | 2 | 100% |
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| 7 | Vladimir Vovk, Alex Gammerman, and Glenn Shafer (2005) Algorithmic learning in a random world | 0.511 | 2 | 1 | 100% |
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| 9 | Peter L. Bartlett, Philip M. Long, Gábor Lugosi, and Alexander Tsigler (2020) Benign overfitting in linear regression | 0.405 | 1 | 1 | 100% |
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