Summary of Conformal Structured Prediction, by Botong Zhang et al.
Conformal Structured Prediction
by Botong Zhang, Shuo Li, Osbert Bastani
First submitted to arxiv on: 8 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 Medium Difficulty summary: Conformal prediction has gained traction as a method for quantifying model uncertainty, producing sets of labels with high probability of containing the true label. While existing algorithms focus on classification and regression tasks, structured output settings like text generation require more nuanced approaches. This paper proposes a framework for conformal prediction in structured settings, modifying existing methods to generate sets that implicitly represent potential labels. The approach is demonstrated in domains where prediction sets can be represented as directed acyclic graphs, such as hierarchical image classification. The authors show how their algorithm satisfies coverage guarantees in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper explores a new way to understand the uncertainty of predictive models. Usually, these models just give you one answer, but this approach gives multiple possible answers with high accuracy. Right now, this works well for simple tasks like classifying pictures or predicting numbers. However, for more complex tasks like generating text, we need a better approach. This paper proposes a new way to do conformal prediction, which helps us generate sets of possible answers that work well in different situations. |
Keywords
» Artificial intelligence » Classification » Image classification » Probability » Regression » Text generation
