Researchers have developed HOST, a new algorithm for learning Gaussian Directed Acyclic Graphs (DAGs) that addresses the statistical-computational gap. Unlike existing methods that require computationally expensive subset searches or have less favorable sample complexity, HOST uses nodewise hold-out scoring and convex regression. This approach achieves competitive graph recovery in polynomial time with a sample complexity of order $d\log p$ for a $p$-node DAG of maximum indegree $d$. Experiments indicate that HOST scales favorably in terms of runtime while maintaining strong graph recovery performance. AI
IMPACT Improves efficiency in learning complex graphical models, potentially benefiting areas relying on causal inference.
RANK_REASON The cluster describes a new algorithm presented in an arXiv paper for a specific machine learning task. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- directed acyclic graph
- Gaussian DAG
- Gotit.pub
- Hugging Face
- ScienceCast
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
