# An Elementary Predictor Obtaining 2√T+1 Distance to Calibration

arXiv: https://arxiv.org/abs/2402.11410 · HTML: https://mlsanity.com/paper/2402.11410/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [A Unifying Theory of Distance from Calibration](https://mlsanity.com/paper/2211.16886/) (2022)
- [Stronger Calibration Lower Bounds via Sidestepping](https://mlsanity.com/paper/2012.03454/) (2020)
- [Smooth Calibration, Leaky Forecasts, Finite Recall, and Nash Dynamics](https://mlsanity.com/paper/2210.07152/) (2022)
- [On the Distance from Calibration in Sequential Prediction](https://mlsanity.com/paper/2402.07458/) (2024)
- [Faster online calibration without randomization: interval forecasts and the power of two choices](https://mlsanity.com/paper/2204.13087/) (2022)

## Built on by (most cited first, 1 of 1)

- [Truthfulness of Calibration Measures](https://mlsanity.com/paper/2407.13979/) (2024)

## Abstract (arXiv)

Blasiok et al. [2023] proposed distance to calibration as a natural measure of calibration error that unlike expected calibration error (ECE) is continuous. Recently, Qiao and Zheng [2024] gave a non-constructive argument establishing the existence of an online predictor that can obtain O(√T) distance to calibration in the adversarial setting, which is known to be impossible for ECE. They leave as an open problem finding an explicit, efficient algorithm. We resolve this problem and give an extremely simple, efficient, deterministic algorithm that obtains distance to calibration error at most 2√T+1.