Summary of Adaptive Conformal Inference by Betting, By Aleksandr Podkopaev et al.
Adaptive Conformal Inference by Betting
by Aleksandr Podkopaev, Darren Xu, Kuang-Chih Lee
First submitted to arxiv on: 26 Dec 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 The proposed method in this paper tackles the problem of adaptive conformal inference for machine learning models, eliminating the assumption of data exchangeability. The existing approaches rely on optimizing the pinball loss using online gradient descent, but are sensitive to the choice of learning rates. This paper presents a novel approach that leverages parameter-free online convex optimization techniques to achieve adaptive conformal inference without requiring cumbersome parameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning by making it possible to predict how well a model will work without needing special assumptions about the data. Right now, there are ways to do this but they rely on choosing the right “learning rate” which can be tricky. The new method doesn’t need that and works well. |
Keywords
» Artificial intelligence » Gradient descent » Inference » Machine learning » Optimization
