Summary of Conformal Prediction: a Data Perspective, by Xiaofan Zhou et al.
Conformal Prediction: A Data Perspective
by Xiaofan Zhou, Baiting Chen, Yu Gui, Lu Cheng
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 conformal prediction (CP) framework provides reliable predictive inference for black-box models by constructing prediction sets that contain the true output with a specified probability. While traditional CP methods have been challenged by modern data science’s diverse modalities, increasing data complexity, and growing model sophistication, recent advancements address evolving scenarios. This survey reviews foundational CP concepts and novel approaches from a data-centric perspective, including applications to structured, unstructured, and dynamic data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make predictions with computers that can be trusted. It’s called conformal prediction, or CP for short. Think of it like a box that contains the correct answer with a certain level of confidence. The problem is that new kinds of data and models are making this process harder. But some clever people have come up with new ideas to solve these challenges. This paper looks at what we already know about CP, and how we can apply it to different types of data. |
Keywords
» Artificial intelligence » Inference » Probability
