# Online Conformal Prediction via Universal Portfolio Algorithms

arXiv: https://arxiv.org/abs/2602.03168 · HTML: https://mlsanity.com/paper/2602.03168/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2026.

## Built on by (most cited first, 4 of 4)

- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [Online Localized Conformal Prediction](https://mlsanity.com/paper/2605.05497/) (2026)
- [Is Online Linear Optimization Sufficient for Strategic Robustness?](https://mlsanity.com/paper/2602.12253/) (2026)
- [Parameter-Free and Group Conditional Online Conformal Prediction](https://mlsanity.com/paper/2606.00419/) (2026)

## Abstract (arXiv)

Online conformal prediction (OCP) seeks prediction intervals that achieve long-run 1-α coverage for arbitrary (possibly adversarial) data streams, while remaining as informative as possible. Existing OCP methods often require manual learning-rate tuning to work well, and may also require algorithm-specific analyses. Here, we develop a general regret-to-coverage theory for interval-valued OCP based on the (1-α)-pinball loss. Our first contribution is to identify linearized regret as a key notion, showing that controlling it implies coverage bounds for any online algorithm. This relies on a black-box reduction that depends only on the Fenchel conjugate of an upper bound on the linearized regret. Building on this theory, we propose UP-OCP, a parameter-free method for OCP, via a reduction to a two-asset portfolio selection problem, leveraging universal portfolio algorithms. We show strong finite-time bounds on the miscoverage of UP-OCP, even for polynomially growing predictions. Extensive experiments support that UP-OCP delivers consistently better size/coverage trade-offs than prior online conformal baselines.