Summary of Single Trajectory Conformal Prediction, by Brian Lee and Nikolai Matni
Single Trajectory Conformal Prediction
by Brian Lee, Nikolai Matni
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); 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 This paper examines the performance of risk-controlling prediction sets (RCPS), an empirical risk minimization-based approach to conformal prediction. The authors investigate RCPS using a single trajectory of temporally correlated data from an unknown stochastic dynamical system. They utilize two techniques, blocking and decoupling, to analyze the guarantees provided by RCPS in various scenarios. The results demonstrate that RCPS can attain similar performance guarantees as those enjoyed in the independent and identically distributed (iid) setting when the data is generated by asymptotically stationary and contractive dynamics. Additionally, the authors show how these techniques can be used to study online and offline conformal prediction algorithms, which are currently analyzed using different tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a special kind of prediction method called risk-controlling prediction sets (RCPS). It uses data from an unknown system that changes over time. The researchers test RCPS with two techniques: blocking and decoupling. They find that when the data is generated by a stable system, RCPS works well. When the system changes, RCPS’s guarantees get weaker. This work can help us understand how to analyze different types of prediction methods. |
