Summary of Automatically Adaptive Conformal Risk Control, by Vincent Blot (lisn et al.
Automatically Adaptive Conformal Risk Control
by Vincent Blot, Anastasios N Angelopoulos, Michael I Jordan, Nicolas J-B Brunel
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses a pressing issue in machine learning: ensuring reliable performance from black-box algorithms under various input conditions. Traditional approaches rely on user-defined conditioning events, but this can be limiting. Building upon recent work, the authors propose a methodology to adaptively control statistical risks by determining appropriate function classes for conditioning based on test sample difficulty. This framework goes beyond traditional methods and is applied to regression and segmentation tasks, demonstrating superior precision through continuous monitoring and adjustment of parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to make sure a machine learning algorithm works well under different conditions. It’s like trying to predict what will happen when you give it different pictures or data. The problem is that current methods rely on someone telling the algorithm what kind of picture it might get, but this isn’t always possible. The authors of this paper propose a new way to make sure the algorithm works well by letting it figure out what kind of picture it’s looking at and adjusting its performance accordingly. They tested this approach on various tasks and showed that it can be more accurate than traditional methods. |
Keywords
» Artificial intelligence » Machine learning » Precision » Regression
