A Correction of Pseudo Log-Likelihood Method
Machine Learning
2024-03-28 v1 Statistics Theory
Machine Learning
Statistics Theory
Abstract
Pseudo log-likelihood is a type of maximum likelihood estimation (MLE) method used in various fields including contextual bandits, influence maximization of social networks, and causal bandits. However, in previous literature \citep{li2017provably, zhang2022online, xiong2022combinatorial, feng2023combinatorial1, feng2023combinatorial2}, the log-likelihood function may not be bounded, which may result in the algorithm they proposed not well-defined. In this paper, we give a counterexample that the maximum pseudo log-likelihood estimation fails and then provide a solution to correct the algorithms in \citep{li2017provably, zhang2022online, xiong2022combinatorial, feng2023combinatorial1, feng2023combinatorial2}.
Keywords
Cite
@article{arxiv.2403.18127,
title = {A Correction of Pseudo Log-Likelihood Method},
author = {Shi Feng and Nuoya Xiong and Zhijie Zhang and Wei Chen},
journal= {arXiv preprint arXiv:2403.18127},
year = {2024}
}
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7 pages