Summary of Online Scalable Gaussian Processes with Conformal Prediction For Guaranteed Coverage, by Jinwen Xu et al.
Online scalable Gaussian processes with conformal prediction for guaranteed coverage
by Jinwen Xu, Qin Lu, Georgios B. Giannakis
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); Machine Learning (stat.ML)
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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 A novel Bayesian nonparametric framework is proposed to address uncertainty quantification (UQ) in safety-critical applications, such as robotics and healthcare. The Gaussian process (GP) is widely used for UQ, but its consistency relies on assumptions about the learning function’s properties. To combat model mis-specification, a conformal prediction (CP) approach is integrated with GP to produce distribution-free prediction sets. However, this assumes data exchangeability, which is often violated in online settings where predictions are made before true labels are revealed. An adaptive threshold parameter is introduced to ensure long-term coverage guarantees. Experimental results demonstrate the superiority of the proposed online GP-CP approach over existing methods in terms of long-term coverage performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict uncertainty in important situations like robotics and healthcare is presented. The Gaussian process (GP) is often used, but it assumes certain things about the data are true. To fix this, a technique called conformal prediction (CP) is combined with GP to make more reliable predictions. However, CP assumes that the data can be swapped between different groups without changing the results. This isn’t always true, especially when making predictions before knowing the correct answer. To solve this, an adjustment is made to ensure that the predictions remain accurate over time. The results show that this new approach works better than others in keeping the uncertainty predictions accurate. |
