A Log-Linear Time Sequential Optimal Calibration Algorithm for Quantized Isotonic L2 Regression
Machine Learning
2022-06-03 v1 Optimization and Control
Machine Learning
Abstract
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.
Cite
@article{arxiv.2206.00744,
title = {A Log-Linear Time Sequential Optimal Calibration Algorithm for Quantized Isotonic L2 Regression},
author = {Kaan Gokcesu and Hakan Gokcesu},
journal= {arXiv preprint arXiv:2206.00744},
year = {2022}
}