# A Correction of Pseudo Log-Likelihood Method

arXiv: https://arxiv.org/abs/2403.18127 · HTML: https://mlsanity.com/paper/2403.18127/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Combinatorial Causal Bandits](https://mlsanity.com/paper/2206.01995/) (2022)
- [Combinatorial Pure Exploration of Causal Bandits](https://mlsanity.com/paper/2206.07883/) (2022)
- [Combinatorial Causal Bandits without Graph Skeleton](https://mlsanity.com/paper/2301.13392/) (2023)

## Built on by (most cited first, 1 of 1)

- [Contextual Procurement Auctions with Bandit Learning](https://mlsanity.com/paper/2607.05813/) (2026)

## Abstract (arXiv)

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 citepli2017provably, 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 citepli2017provably, zhang2022online, xiong2022combinatorial, feng2023combinatorial1, feng2023combinatorial2.