# Online Platt Scaling with Calibeating

arXiv: https://arxiv.org/abs/2305.00070 · HTML: https://mlsanity.com/paper/2305.00070/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Verified Uncertainty Calibration](https://mlsanity.com/paper/1909.10155/) (2019)
- [Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning](https://mlsanity.com/paper/2003.07329/) (2020)
- [Mitigating Bias in Calibration Error Estimation](https://mlsanity.com/paper/2012.08668/) (2020)
- [Calibration tests in multi-class classification: A unifying framework](https://mlsanity.com/paper/1910.11385/) (2019)
- [Distribution-free binary classification: prediction sets, confidence intervals and calibration](https://mlsanity.com/paper/2006.10564/) (2020)
- [Top-label calibration and multiclass-to-binary reductions](https://mlsanity.com/paper/2107.08353/) (2021)
- [Distribution-free calibration guarantees for histogram binning without sample splitting](https://mlsanity.com/paper/2105.04656/) (2021)
- [Logistic Regression: The Importance of Being Improper](https://mlsanity.com/paper/1803.09349/) (2018)
- [Forecast Hedging and Calibration](https://mlsanity.com/paper/2210.07169/) (2022)
- [Approachability, Regret and Calibration; implications and equivalences](https://mlsanity.com/paper/1301.2663/) (2013)
- [Efficient improper learning for online logistic regression](https://mlsanity.com/paper/2003.08109/) (2020)
- ["Calibeating": Beating Forecasters at Their Own Game](https://mlsanity.com/paper/2209.04892/) (2022)
- [Mixability made efficient: Fast online multiclass logistic regression](https://mlsanity.com/paper/2110.03960/) (2021)
- [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, 8 of 8)

- [Minimum-Risk Recalibration of Classifiers](https://mlsanity.com/paper/2305.10886/) (2023)
- [Orthogonal Causal Calibration](https://mlsanity.com/paper/2406.01933/) (2024)
- [Calibeating Made Simple](https://mlsanity.com/paper/2603.22167/) (2026)
- [Parity Calibration](https://mlsanity.com/paper/2305.18655/) (2023)
- [Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling](https://mlsanity.com/paper/2502.15131/) (2025)
- [Learning to Decide with AI Assistance under Human-Alignment](https://mlsanity.com/paper/2605.12646/) (2026)
- [Protected Probabilistic Classification Library](https://mlsanity.com/paper/2509.11267/) (2025)
- [Calibeating for general proper losses: A Bregman divergence approach](https://mlsanity.com/paper/2605.17269/) (2026)

## Abstract (arXiv)

We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OPS by incorporating a recently developed technique called calibeating to make it more robust. Theoretically, our resulting OPS+calibeating method is guaranteed to be calibrated for adversarial outcome sequences. Empirically, it is effective on a range of synthetic and real-world datasets, with and without distribution drifts, achieving superior performance without hyperparameter tuning. Finally, we extend all OPS ideas to the beta scaling method.