Summary of Mutual Information Multinomial Estimation, by Yanzhi Chen et al.
Mutual Information Multinomial Estimation
by Yanzhi Chen, Zijing Ou, Adrian Weller, Yingzhen Li
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); 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 The proposed new estimator for mutual information (MI) is a significant advancement in data science and machine learning, as estimating MI is crucial for understanding relationships between variables. The key innovation lies in utilizing a preliminary estimate of the data distribution to bridge the gap between joint and marginal distributions, enabling accurate estimation of the true difference between these two distributions. Experimental results on diverse tasks, including synthetic problems with known ground-truth and real-world applications, demonstrate the effectiveness of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mutual information (MI) is a fundamental concept in data science that helps us understand how variables are related. Researchers have been trying to find new ways to estimate MI, which can be tricky. This paper proposes a new method that uses a preliminary estimate of the data distribution to help calculate MI. Essentially, this bridge allows us to compare joint distributions with marginal distributions more accurately. The authors tested their approach on various tasks and found it worked well for both synthetic and real-world data. |
Keywords
» Artificial intelligence » Machine learning
