Research map / Learning theory and algorithms / Kernels and regression theory
One-shot Distributed Ridge Regression in High Dimensions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use optimal weights (sum not constrained to one) instead of uniform averaging of local ridge estimators | one-shot distributed ridge regression · Million Song Dataset (MSD) | test MSE | Helped |
| Optimally weighted estimator vs naive averaging as number of machines grows | one-shot distributed ridge regression · Million Song Dataset (MSD) | test MSE | Helped |
| Data splitting across up to 100 machines in low-SNR regime (alpha^2 about 0.3) | optimally weighted distributed ridge · Million Song Dataset (MSD) | test MSE | No clear effect |
| Lower signal strength (smaller alpha^2) and higher aspect ratio gamma | one-shot distributed ridge efficiency (ARE) · theory (asymptotic relative efficiency) | ARE relative to centralized ridge | Helped |
| Increasing number of machines k | one-shot distributed ridge efficiency (ARE) · theory (asymptotic relative efficiency) | ARE relative to centralized ridge | Hurt |
| Optimal weights not constrained to sum to one (vs. uniform averaging) | one-shot distributed ridge weights · theory | sum of optimal weights | Helped |
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