Summary of Classification Under Nuisance Parameters and Generalized Label Shift in Likelihood-free Inference, by Luca Masserano et al.
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference
by Luca Masserano, Alex Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper tackles the challenge of classifying events with reliable uncertainty measures when there’s a distributional shift between train and target data. The authors propose a new method to overcome biased predictions and invalid uncertainty estimates by casting classification as a hypothesis testing problem under nuisance parameters. They develop a receiver operating characteristic (ROC) approach that allows for domain adaptation and valid prediction sets while maintaining high power. This innovative method is demonstrated on two scientific problems in biology and astroparticle physics with realistic mechanistic models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps scientists predict events more accurately by accounting for uncertainties. It’s about how to classify things when the rules are different between what you know and what you’re trying to figure out. The authors came up with a new way to make predictions that takes into account these changes, which is important because it can help us better understand things like biology and space. |
Keywords
* Artificial intelligence * Classification * Domain adaptation
