Summary of An Asymptotically Optimal Coordinate Descent Algorithm For Learning Bayesian Networks From Gaussian Models, by Tong Xu et al.
An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models
by Tong Xu, Simge Küçükyavuz, Ali Shojaie, Armeen Taeb
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a new algorithm for learning Bayesian networks from continuous observational data, generated according to a linear Gaussian structural equation model. The goal is to find an _0-penalized maximum likelihood estimator, which has favorable statistical properties but can be computationally challenging to solve. To address this issue, the authors introduce a coordinate descent algorithm that converges to a coordinate-wise minimum and achieves optimality and statistical consistency guarantees. This is the first coordinate descent procedure with such guarantees in learning Bayesian networks. The algorithm is demonstrated to be scalable and effective on both synthetic and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to understand complex systems by using special math tools called Bayesian networks. These networks are like maps that show how different things are related. We can use these networks to make predictions about what might happen in the future based on what we know now. The problem is that it’s hard to figure out these networks when we only have some data and not all of it. So, the authors came up with a new way to solve this problem using something called coordinate descent. This method helps us find the best possible network by looking at one part at a time. They showed that this method works well on fake data and real-world data too! |
Keywords
» Artificial intelligence » Likelihood
