Summary of Markov Equivalence and Consistency in Differentiable Structure Learning, by Chang Deng et al.
Markov Equivalence and Consistency in Differentiable Structure Learning
by Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 issues in differentiable structure learning of directed acyclic graphs (DAGs). Current approaches rely on strong identifiability assumptions, which may not hold in practice. Additionally, optimizers can exploit undesirable artifacts in the loss function, leading to suboptimal solutions. The authors propose a regularization technique for the likelihood function that identifies the sparsest model in the Markov equivalence class, even without an identifiable parametrization. They demonstrate this approach on Gaussian models and then generalize it to broader models and likelihoods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves problems with learning directed graphs. Right now, we can’t always know which graph is “true” because our methods assume too much about the data. Sometimes, these assumptions aren’t met in real-world situations. The authors figured out how to fix this by adjusting the way we measure how good a solution is. This allows us to find the simplest possible graph that still fits the data. |
Keywords
» Artificial intelligence » Likelihood » Loss function » Regularization
