mlsanityData to 14 Oct 2026

A Log-Linear Time Sequential Optimal Calibration Algorithm for Quantized Isotonic L2 Regression

arXiv 2206.00744 ↗First posted 1 Jun 2022cs.LG math.OC stat.MLcited by 1

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Abstract (arXiv)

We study the sequential calibration of estimations in a quantized isotonic L2 regression setting. We start by showing that the optimal calibrated quantized estimations can be acquired from the traditional isotonic L2 regression solution. We modify the traditional PAVA algorithm to create calibrators for both batch and sequential optimization of the quantized isotonic regression problem. Our algorithm can update the optimal quantized monotone mapping for the samples observed so far in linear space and logarithmic time per new unordered sample.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 49 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

5 of 49 references.

References · 5

  1. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
  2. 2015Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Modelscited by 21
  3. 2018Sequential Outlier Detection based on Incremental Decision Treescited by 21
  4. 2021Optimally Efficient Sequential Calibration of Binary Classifiers to Minimize Classification Errorcited by 17
  5. 2021Efficient, Anytime Algorithms for Calibration with Isotonic Regression under Strictly Convex Lossescited by 5

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Sequential Linearithmic Time Optimal Unimodal Fitting When Minimizing Univariate Linear Lossescited by 0

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