# Calibeating Made Simple

arXiv: https://arxiv.org/abs/2603.22167 · HTML: https://mlsanity.com/paper/2603.22167/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 10 of 10)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications](https://mlsanity.com/paper/2108.03837/) (2021)
- [Full Swap Regret and Discretized Calibration](https://mlsanity.com/paper/2502.09332/) (2025)
- ["Calibeating": Beating Forecasters at Their Own Game](https://mlsanity.com/paper/2209.04892/) (2022)
- [Optimal Multiclass U-Calibration Error and Beyond](https://mlsanity.com/paper/2405.19374/) (2024)
- [Faster Recalibration of an Online Predictor via Approachability](https://mlsanity.com/paper/2310.17002/) (2023)
- [Simultaneous Swap Regret Minimization via KL-Calibration](https://mlsanity.com/paper/2502.16387/) (2025)
- [Online Platt Scaling with Calibeating](https://mlsanity.com/paper/2305.00070/) (2023)
- [Calibrated Probabilistic Forecasts for Arbitrary Sequences](https://mlsanity.com/paper/2409.19157/) (2024)
- [Calibrated Regression Against An Adversary Without Regret](https://mlsanity.com/paper/2302.12196/) (2023)

## Built on by (most cited first, 5 of 5)

- [Proper Calibeating](https://mlsanity.com/paper/2605.26703/) (2026)
- [Optimal Recalibration of an Online Predictor](https://mlsanity.com/paper/2607.19689/) (2026)
- [Calibeating for general proper losses: A Bregman divergence approach](https://mlsanity.com/paper/2605.17269/) (2026)
- [Dirichlet Follow-the-Leader Closes the Gap in Simultaneous Multiclass U-Calibration](https://mlsanity.com/paper/2608.06656/) (2026)
- [TCSAlgBench: Benchmarking Automated Proving for Research-Level Theoretical Computer Science](https://mlsanity.com/paper/2609.35606/) (2026)

## Abstract (arXiv)

We study calibeating, the problem of post-processing external forecasts online to minimize cumulative losses and match an informativeness-based benchmark. Unlike prior work, which analyzed calibeating for specific losses with specific arguments, we reduce calibeating to existing online learning techniques and obtain results for general proper losses. More concretely, we first show that calibeating is minimax-equivalent to regret minimization. This recovers the O(log T) calibeating rate of Foster and Hart [FH23] for the Brier and log losses and its optimality, and yields new optimal calibeating rates for mixable losses and general bounded losses. Second, we prove that multi-calibeating is minimax-equivalent to the combination of calibeating and the classical expert problem. This yields new optimal multi-calibeating rates for mixable losses, including Brier and log losses, and general bounded losses. Finally, we obtain new bounds for achieving calibeating and calibration simultaneously for the Brier loss. For binary predictions, our result gives the first calibrated algorithm that at the same time also achieves the optimal O(log T) calibeating rate.