Research map / Learning theory and algorithms / Matrix and tensor methods
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Pool auxiliary-task samples and estimate a single common precision matrix | Support-union recovery · Auxiliary tasks | sufficient samples per task: O((log N)/K) | Helped |
| Use auxiliary-task samples to estimate the support union before learning the novel task | Novel-task support recovery · Novel task | sufficient samples: O(log(|S_off|)) | Helped |
| Constrain novel-task support to the estimated union and fix its diagonal | Novel-task precision estimation · Novel task | sufficient samples for support recovery: O(log(|S_off|)) | Helped |
| Use more auxiliary tasks with n=(200 log N)/K samples per task | Meta learning support-union estimator · Synthetic Erdős–Rényi graphs | support-union recovery probability | Helped |
| Use more auxiliary tasks | L1Inf, L12, Cooperative-LASSO, and Joint baselines · Synthetic Erdős–Rényi graphs | support-union recovery probability | Hurt |
| Use the meta-learning estimator instead of the multi-task baselines | Support-union recovery · Synthetic graphs, fixed K=10 and n=(C log N)/K | support-union recovery probability | Helped |
| Use the meta-learning estimator instead of the compared baselines | Task 8 precision estimation · Real-world data, task 8 | negative log-determinant Bregman divergence: -47 vs -179, -100, -85, -534, -324 | Mixed |
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