Sequential Linearithmic Time Optimal Unimodal Fitting When Minimizing Univariate Linear Losses
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Abstract (arXiv)
This paper focuses on optimal unimodal transformation of the score outputs of a univariate learning model under linear loss functions. We demonstrate that the optimal mapping between score values and the target region is a rectangular function. To produce this optimal rectangular fit for the observed samples, we propose a sequential approach that can its estimation with each incoming new sample. Our approach has logarithmic time complexity per iteration and is optimally efficient.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 44 references that are in the map. First posted on arXiv 4 Apr 2023.
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9 of 44 references.
References · 9
- 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
- 2015Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Modelscited by 21
- 2018Sequential Outlier Detection based on Incremental Decision Treescited by 21
- 2021Optimally Efficient Sequential Calibration of Binary Classifiers to Minimize Classification Errorcited by 17
- 2020Recursive Experts: An Efficient Optimal Mixture of Learning Systems in Dynamic Environmentscited by 15
- 2021Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clusteringcited by 12
- 2022Low Regret Binary Sampling Method for Efficient Global Optimization of Univariate Functionscited by 11
- 2021Efficient, Anytime Algorithms for Calibration with Isotonic Regression under Strictly Convex Lossescited by 5
- 2022A Log-Linear Time Sequential Optimal Calibration Algorithm for Quantized Isotonic L2 Regressioncited by 1
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