Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian Joint Spike-and-Slab Graphical Lasso
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DSS-FGL precision estimates instead of FGL estimates | Missing mortality rate imputation · Missing mortality rates | Average MSE: 0.00348 -> 0.00268 | Helped |
| Model residual dependence with DSS-FGL rather than assuming i.i.d. errors | Missing mortality rate imputation · Missing mortality rates | Average MSE: 0.00372 -> 0.00268; 27.8% | Helped |
| Use a single spike-and-slab mixture rather than the doubly spike-and-slab formulation (ablation) | DSS-FGL · Two-class simulated graphical model | False-positive edges | Hurt |
Closest papers by meaning
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- A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models (ICML 2018 · similarity 0.87)
- Select-and-Sample for Spike-and-Slab Sparse Coding (NeurIPS 2016 · similarity 0.87)
- Training Bayesian Neural Networks with Sparse Subspace Variational Inference (ICLR 2024 · similarity 0.86)
- Posterior Contraction Rates for sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces (NeurIPS 2026 · similarity 0.85)
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