Research map / Learning theory and algorithms / Matrix and tensor methods
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models
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 L0-L2 constrained estimators instead of L1-based procedures | Ising graph learning, Examples 1 and 2 at both graph sizes · Examples 1 and 2, both graph sizes | L2 estimation | Helped |
| Use L0-L2 constrained estimators instead of the compared estimators | Ising graph learning · Phase-transition experiments, every graph size | phase-transition sharpness | Helped |
Closest papers by meaning
- Limits on Testing Structural Changes in Ising Models (NeurIPS 2020 · similarity 0.89)
- Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model (NeurIPS 2020 · similarity 0.88)
- Generalized Direct Change Estimation in Ising Model Structure (ICML 2016 · similarity 0.87)
- Fast and Consistent Structure Learning in Graphical Models via Approximate Cross-Validation (NeurIPS 2026 · similarity 0.87)
- Markov Equivalence and Consistency in Differentiable Structure Learning (NeurIPS 2024 · similarity 0.85)
- Global Minimizers of $\ell^p$-Regularized Objectives Yield the Sparsest ReLU Neural Networks (NeurIPS 2025 · similarity 0.85)
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.