Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian Joint Estimation of Multiple Graphical Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| joint spike-and-slab group prior over classes instead of fitting each class separately (BAGUS) | BAGUS · Nearest-neighbor rho=0 | MCC: 0.794(0.033) -> 0.955(0.022) | Helped |
| share structure across classes instead of pooling all classes into one model | BAGUS · Nearest-neighbor rho=0 | F-norm: 7.115(0.380) -> 2.576(0.221) | Helped |
| use alpha=n instead of alpha=1 (ablation) | Joint spike-and-slab MAP · Nearest-neighbor rho=0 | MCC: 0.955(0.022) -> 0.991(0.009) | Helped |
| use alpha=n instead of alpha=1 (ablation) | Joint spike-and-slab MAP · Scale-free rho=0 | MCC: 0.993(0.006) -> 0.906(0.047) | Hurt |
| use alpha=n instead of alpha=1 (ablation) | Joint spike-and-slab MAP · Scale-free rho=0.5 | MCC: 0.719(0.018) -> 0.696(0.036) | Hurt |
| joint estimation when sparsity patterns differ (rho=0.5) | BAGUS · Scale-free rho=0.5 | MCC: 0.716(0.028) -> 0.719(0.018) | No clear effect |
| joint method vs GGL in runtime | GGL · Nearest-neighbor rho=0 | seconds: 8.715(0.314) -> 3.667(0.040) | Helped |
Closest papers by meaning
- Bayesian Joint Spike-and-Slab Graphical Lasso (ICML 2019 · similarity 0.92)
- A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models (ICML 2018 · similarity 0.87)
- T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs (NeurIPS 2021 · similarity 0.87)
- Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model (NeurIPS 2020 · similarity 0.87)
- Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization (NeurIPS 2017 · similarity 0.86)
- Learning Local Neighborhoods of Non-Gaussian Graphical Models (AAAI 2025 · similarity 0.85)
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