Summary of Variational Dag Estimation Via State Augmentation with Stochastic Permutations, by Edwin V. Bonilla et al.
Variational DAG Estimation via State Augmentation With Stochastic Permutations
by Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O’Kane
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes an innovative approach to estimating the structure of Bayesian networks from observational data, which is crucial in areas like causal discovery. The authors leverages Bayesian methods to quantify uncertainty and address identifiability issues. To overcome the challenges of representing distributions over graphs that satisfy the directed acyclic graph (DAG) constraint and estimating a posterior over the underlying combinatorial space, they introduce an augmented space of DAGs and permutations. By applying variational inference with continuous relaxations of discrete distributions, the authors demonstrate competitive performance on synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to figure out the structure of something called a Bayesian network from just observations. This is important because it can help us discover causes and effects in many areas. The researchers are using special math tools called Bayesian methods, which allow us to measure uncertainty and solve some tricky problems. They’re also working on a new way to represent these complex structures and calculate the possibilities. By doing this, they were able to do as well or better than other approaches on some test datasets. |
Keywords
* Artificial intelligence * Bayesian network * Inference
