Summary of The Benefit Of Being Bayesian in Online Conformal Prediction, by Zhiyu Zhang et al.
The Benefit of Being Bayesian in Online Conformal Prediction
by Zhiyu Zhang, Zhou Lu, Heng Yang
First submitted to arxiv on: 3 Oct 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 research paper investigates the construction of valid confidence sets for machine learning models without any prior knowledge about the underlying data distribution. The authors propose a novel approach based on Conformal Prediction (CP) and demonstrate its effectiveness in reducing the impact of statistical assumptions on the obtained confidence sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents two distinct approaches to solve this problem: the direct approach, which assumes the data sequence is independent and identically distributed (i.i.d.) or exchangeable; and the indirect approach, which applies first-order online optimization to moving quantile losses. However, the latter method requires knowing the target quantile level beforehand and may not provide valid confidence sets due to linearization issues. |
Keywords
* Artificial intelligence * Machine learning * Optimization
