Summary of Non-asymptotic Uncertainty Quantification in High-dimensional Learning, by Frederik Hoppe et al.
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
by Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus, Felix Krahmer, Holger Rauhut
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Image and Video Processing (eess.IV); Applications (stat.AP); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Uncertainty quantification (UQ) is a crucial task in high-dimensional regression or learning problems to increase confidence in predictors. This paper develops a new data-driven approach for UQ in regression, applicable to classical approaches like LASSO and neural networks. The debiased LASSO method constructs asymptotic confidence intervals by decomposing estimation error into Gaussian and bias components. However, the bias term is often significant in real-world problems, leading to overly narrow confidence intervals. Our work addresses this issue by deriving a data-driven adjustment that corrects confidence intervals for a large class of predictors by estimating mean and variance of bias terms from training data. This yields non-asymptotic confidence intervals, avoiding uncertainty overestimation in applications like MRI diagnosis. The analysis extends beyond sparse regression to neural networks, enhancing the reliability of deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine trying to predict something with high accuracy, but you’re not sure how confident you are. This is called uncertainty quantification (UQ). Researchers have developed new methods for UQ in machine learning, which can be used in many different areas like medical diagnosis. The old way of doing this was limited because it didn’t account for a key problem that makes predictions less accurate than they should be. Our team has solved this issue by creating a new method that corrects for this problem and provides more realistic confidence levels. This is important because overestimating uncertainty can lead to poor decisions. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Regression
