Parameter-Free and Group Conditional Online Conformal Prediction

2026-05-29

AI Summary

Existing online conformal prediction methods struggle to simultaneously achieve parameter-free operation and rigorous error control under group-conditional settings in non-stationary data streams, limiting the fairness and robustness of uncertainty quantification. This work proposes the first online conformal prediction algorithm that unifies parameter-free online optimization with group-conditional coverage guarantees. The method requires no learning rate tuning and provides strict conditional coverage for distinct data groups even in dynamic environments. Empirical evaluations demonstrate that the proposed approach substantially improves the reliability of current parameter-free methods while delivering prediction interval quality comparable to carefully tuned group-conditional baselines.

Abstract

Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data may not be exchangeable). Online conformal prediction (OCP) methods address this issue at the expense of either (i) group-wise error control or (ii) learning-rate independent implementation. Group-conditional coverage is essential for fairness across different collections of data points and for providing finer UQ guarantees. Parameter-free optimization is crucial for robustness to adversarial and unknown data shifts. We propose a parameter-free algorithm for group-conditional OCP and demonstrate that it achieves the best group-conditional coverage guarantees.We evaluate our algorithm on synthetic and real-world data, demonstrating that our method not only improves the reliability of existing parameter-free OCP methods but also provides prediction intervals that are comparable in size to well-tuned group-conditional approaches. By unifying group-conditional coverage with parameter-free online algorithms, our work lays a foundation for fair and robust uncertainty quantification in shifting environments.

Problem

• online conformal prediction
• group-conditional coverage
• parameter-free
• uncertainty quantification
• distribution shift

Innovation

• parameter-free
• group-conditional coverage
• online conformal prediction
• uncertainty quantification
• distribution shift