Research map / Learning theory and algorithms / Sample complexity
Learning Bayesian Networks in the Presence of Structural Side Information
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RSL_D instead of state-of-the-art structure learning algorithms | BN structure learning on random Erdos-Renyi graphs with finite samples · Erdos-Renyi G(n,p), finite-sample settings | skeleton accuracy | Helped |
| Use RSL_ω instead of state-of-the-art structure learning algorithms | BN structure learning on random Erdos-Renyi graphs with finite samples · Erdos-Renyi G(n,p), finite-sample settings | skeleton accuracy | Helped |
| Use RSL_D instead of other compared algorithms | BN structure learning · Erdos-Renyi G(n,p), finite-sample settings | computational complexity | Helped |
| Use RSL_ω instead of other compared algorithms | BN structure learning · Erdos-Renyi G(n,p), finite-sample settings | computational complexity | Helped |
| Use RSL_D and RSL_ω instead of other compared algorithms | BN structure learning · Diabetes and Andes networks | accuracy | Helped |
| Use RSL_D and RSL_ω instead of other compared algorithms | BN structure learning · Diabetes and Andes networks | computational complexity | Helped |
| Use RSL_D on a graph that is not diamond-free | RSL_D · Andes | accuracy | Helped |
| Use RSL_D on graphs that may contain diamonds | RSL_D · BN structure learning on graphs with diamonds | edge recovery | Helped |
Closest papers by meaning
- Extendable and Iterative Structure Learning Strategy for Bayesian Networks (ICLR 2025 · similarity 0.91)
- Turbocharging Treewidth-Bounded Bayesian Network Structure Learning (AAAI 2021 · similarity 0.91)
- Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings (AAAI 2021 · similarity 0.90)
- Learning Fast-Inference Bayesian Networks (NeurIPS 2021 · similarity 0.90)
- Learning Bayesian Networks with Bounded Tree-width via Guided Search (AAAI 2016 · similarity 0.90)
- Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables (NeurIPS 2016 · similarity 0.90)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.