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A Correction of Pseudo Log-Likelihood Method (2403.18127v1)

Published 26 Mar 2024 in cs.LG, math.ST, stat.ML, and stat.TH

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}.

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References (6)
  1. S. Feng and W. Chen. Combinatorial causal bandits. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37(6), pages 7550–7558, 2023.
  2. Combinatorial causal bandits without graph skeleton. arXiv preprint arXiv:2301.13392v3, 2023.
  3. Provably optimal algorithms for generalized linear contextual bandits. In International Conference on Machine Learning, pages 2071–2080. PMLR, 2017.
  4. N. Xiong and W. Chen. Combinatorial pure exploration of causal bandits. In The Eleventh International Conference on Learning Representations, 2022.
  5. Online influence maximization with node-level feedback using standard offline oracles. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36(8), pages 9153–9161, 2022a.
  6. Online influence maximization under the independent cascade model with node-level feedback, 2022b.
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