Summary of Conformal Predictive Systems Under Covariate Shift, by Jef Jonkers et al.
Conformal Predictive Systems Under Covariate Shift
by Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke
First submitted to arxiv on: 23 Apr 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 The paper proposes a new framework for constructing predictive distributions, called Weighted Conformal Predictive Systems (WCPS). This framework is an extension of the original Conformal Predictive Systems (CPS) and allows for calibrated inference and informative decision-making in scenarios characterized by covariate shifts. The authors leverage likelihood ratios between training and testing covariate distributions to construct nonparametric predictive distributions that can handle covariate shifts. The paper presents theoretical underpinnings and conjectures regarding the validity and efficacy of WCPS, as well as empirical evaluations on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WCPS is a new way to predict things based on data. It helps us make good decisions by giving us a range of possible outcomes instead of just one answer. Usually, predictive systems work best when the data is similar, but WCPS can handle situations where the data changes or “shifts”. The authors used this new system to test it on some fake and real datasets, and it worked pretty well! |
Keywords
» Artificial intelligence » Inference » Likelihood
