Research map / Learning theory and algorithms / Sample complexity
The Complexity of Bayesian Network Learning: Revisiting the Superstructure
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.
Closest papers by meaning
- A Parameterized Theory of PAC Learning (AAAI 2023 · similarity 0.91)
- Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings (AAAI 2021 · similarity 0.91)
- Distribution Learning Meets Graph Structure Sampling (NeurIPS 2025 · similarity 0.89)
- On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry (NeurIPS 2026 · similarity 0.87)
- Improving Decision Trees through the Lens of Parameterized Local Search (NeurIPS 2025 · similarity 0.87)
- The Parameterized Complexity of Computing the VC-Dimension (NeurIPS 2025 · similarity 0.87)
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.
