Summary of Semiparametric Conformal Prediction, by Ji Won Park et al.
Semiparametric conformal prediction
by Ji Won Park, Robert Tibshirani, Kyunghyun Cho
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Many risk-sensitive applications require well-calibrated prediction sets over multiple target variables. This paper proposes an algorithm to construct such sets, accounting for the joint correlation structure of vector-valued non-conformity scores. The approach draws from multivariate quantiles and semiparametric statistics, using vine copulas to flexibly estimate the joint cumulative distribution function (CDF) of the scores. This yields desired coverage and competitive efficiency on real-world regression problems, including those with missing-at-random labels in the calibration set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions by taking into account how different things are related to each other. It’s like trying to guess what will happen next in a story – we need to consider all the little clues that can help us get it right. The researchers used special math tricks to figure out how to make these predictions, and they tested their method on real-world problems to see if it worked well. |
Keywords
» Artificial intelligence » Regression
