Summary of A Correction Of Pseudo Log-likelihood Method, by Shi Feng et al.
A Correction of Pseudo Log-Likelihood Method
by Shi Feng, Nuoya Xiong, Zhijie Zhang, Wei Chen
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles a crucial issue in maximum likelihood estimation (MLE) methods used in contextual bandits, influence maximization of social networks, and causal bandits. Specifically, it addresses the problem of unbounded log-likelihood functions, which can lead to poorly defined algorithms. The authors provide a counterexample demonstrating the failure of pseudo log-likelihood estimation and propose a solution to rectify existing algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper fixes a big problem in how we estimate things called maximum likelihood estimations (MLEs). These MLEs are used in some really cool areas like getting the best actions in different situations, making social networks grow faster, and understanding cause-and-effect. The trouble is that sometimes these MLEs don’t work well because the math behind them isn’t complete. In this paper, the authors show what can go wrong and then give a way to make it better. |
Keywords
* Artificial intelligence * Likelihood * Log likelihood
