Summary of Learnability Of Parameter-bounded Bayes Nets, by Arnab Bhattacharyya et al.
Learnability of Parameter-Bounded Bayes Nets
by Arnab Bhattacharyya, Davin Choo, Sutanu Gayen, Dimitrios Myrisiotis
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); 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 paper explores the computational hardness of learning Bayesian networks from data. It builds upon earlier work by Chickering et al., who showed that given a distribution, it’s NP-hard to decide whether there exists a parameter-bounded Bayesian network that represents it. The authors extend this result and prove the NP-hardness of a promise search variant, where a Bayes net is guaranteed to exist and one must find it. They also provide a positive result on the sample complexity required to recover a parameter-bounded Bayes net close to a given distribution, generalizing earlier work by Brustle et al. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how hard it is to figure out Bayesian networks from data. It’s like trying to solve a puzzle with lots of pieces that need to fit together just right. The researchers show that this problem is very hard to solve on a computer, even if we know the answer exists somewhere. They also find that if we have some hints about where to look, it’s still really tough to find the correct solution. On the bright side, they discover that with enough data, we can learn the right Bayesian network and get close to the original distribution. |
Keywords
» Artificial intelligence » Bayesian network
