Research map / Learning theory and algorithms / Matrix and tensor methods
Robust Estimation of Tree Structured Gaussian Graphical 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 |
|---|---|---|---|
| Allow unknown independent additive noise on the diagonal, without side information | Tree-structured Gaussian graphical models · Noisy covariance recovery | tree identifiability | Mixed |
| Provide a known upper bound on leaf-node noise below the stated threshold | Tree-structured Gaussian graphical models · Noisy covariance recovery with side information | tree identifiability | Helped |
| Require leaf precision diagonals to exceed the absolute values of their adjacent off-diagonal entries | Tree-structured Gaussian graphical models · Noisy covariance recovery with side information | tree identifiability | Helped |
| Provide a lower bound on the minimum eigenvalue and require neighboring-node noise to be below that bound | Tree-structured Gaussian graphical models · Noisy covariance recovery with side information | tree identifiability | Helped |
| Use off-diagonal covariance relationships to classify four-node sets and recover the equivalence class | LearnClusterTree · Noisy covariance recovery | tree recovery: O(n^3) | Helped |
| Run Chow-Liu directly on the noisy covariance matrix | Chow-Liu · Four-node Markov chain example | recovery of a feasible tree | Hurt |
| Raise leaf-neighbor noise to the stated sufficient lower bound while imposing a minimum-eigenvalue constraint | Tree-structured Gaussian graphical models · Noisy covariance recovery with side information | tree identifiability | Hurt |
Closest papers by meaning
- SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples (ICML 2021 · similarity 0.90)
- Learning Gaussian DAG Models without Condition Number Bounds (ICML 2025 · similarity 0.87)
- Gaussian Graphical Modelling Without Independence Assumptions for Uncentered Data (AAAI 2025 · similarity 0.86)
- Distribution Learning Meets Graph Structure Sampling (NeurIPS 2025 · similarity 0.85)
- Learning Local Neighborhoods of Non-Gaussian Graphical Models (AAAI 2025 · similarity 0.85)
- Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention (NeurIPS 2024 · 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.