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Summary
The paper proposes a method for adaptive conformal inference using betting, which achieves long-term coverage guarantee without requiring parameter tuning.
Highlights
- The method uses parameter-free online convex optimization techniques, specifically the Krichevsky-Trofimov (KT) estimator and the Online Newton Step (ONS) method.
- The approach is based on framing the task of constructing adaptive conformal predictors as a problem of learning the (1-α)-quantile of the nonconformity scores in an online fashion.
- The method is shown to be effective in simulations and real-world datasets, including changepoint settings and stock price forecasting.
- The approach is compared to existing methods, including online gradient descent (OGD) and scale-free online gradient descent (SF-OGD).
- The method is shown to be robust to distribution shifts and can handle multi-step forecasting.
- The approach is simple to implement and does not require explicit knowledge of the bound on the nonconformity scores.
Key Insights
- The proposed method achieves long-term coverage guarantee without requiring parameter tuning, making it a practical and accessible choice for applications.
- The use of parameter-free online convex optimization techniques allows the method to adapt to changes in the data distribution without requiring explicit knowledge of the bound on the nonconformity scores.
- The method's ability to handle multi-step forecasting makes it suitable for applications where forecasting is done over a horizon, such as in finance and economics.
- The comparison to existing methods shows that the proposed approach is competitive and can outperform other methods in certain scenarios.
- The method's robustness to distribution shifts makes it suitable for applications where the data distribution is expected to change over time.
- The simplicity of the method makes it easy to implement and integrate into existing workflows.
- The method's ability to provide uncertainty estimates makes it suitable for applications where uncertainty quantification is important, such as in finance and healthcare.
Mindmap
Citation
Podkopaev, A., Xu, D., & Lee, K.-C. (2024). Adaptive Conformal Inference by Betting (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2412.19318