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[2512.07770] Distribution-informed Online Conformal Prediction
Summary
The paper presents Conformal Optimistic Prediction (COP), an online conformal prediction algorithm that improves prediction set accuracy by incorporating data patterns, while ensuring valid coverage even with inaccurate estimates.
Why It Matters
This research addresses the challenge of overly conservative prediction sets in online conformal prediction, particularly in adversarial environments. By introducing COP, the authors enhance the flexibility and accuracy of uncertainty quantification methods, which is crucial for applications in machine learning where reliable predictions are essential.
Key Takeaways
- COP improves prediction accuracy by leveraging underlying data patterns.
- The algorithm maintains valid coverage guarantees despite potential inaccuracies.
- It establishes a joint bound on coverage and regret, confirming its validity.
- Experimental results show COP produces tighter prediction intervals than existing methods.
- COP achieves distribution-free coverage under arbitrary learning rates.
Statistics > Machine Learning arXiv:2512.07770 (stat) [Submitted on 8 Dec 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Distribution-informed Online Conformal Prediction Authors:Dongjian Hu, Junxi Wu, Shu-Tao Xia, Changliang Zou View a PDF of the paper titled Distribution-informed Online Conformal Prediction, by Dongjian Hu and Junxi Wu and Shu-Tao Xia and Changliang Zou View PDF HTML (experimental) Abstract:Conformal prediction provides a pivotal and flexible technique for uncertainty quantification by constructing prediction sets with a predefined coverage rate. Many online conformal prediction methods have been developed to address data distribution shifts in fully adversarial environments, resulting in overly conservative prediction sets. We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule. Through estimated cumulative distribution function of non-conformity scores, COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate. We establish a joint bound on coverage and regret, which further confirms the validity of our approach. We also prove that COP achieves distribution-free, finite-sample coverage under arbitrary learning rates and can converge when scores are $i.i.d.$. The experimental results also show that COP can achieve valid coverage and construct shorter predict...