Research map / Learning theory and algorithms / Kernels and regression theory
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Tune subsample size in a full ridgeless ensemble instead of tuning ridge penalty on the full dataset | Ridge regression · Proportional asymptotics, general feature covariance and signal | squared prediction risk | No clear effect |
| Tune subsample size with full-ensemble GCV, without sample splitting | Full ridge ensemble · Proportional asymptotics, all subsample sizes | GCV risk-estimation error | Helped |
| Use GCV to tune subsample size in a ridgeless full ensemble | Full ridgeless ensemble · Proportional asymptotics | squared prediction risk | Helped |
| Use GCV with an ensemble size of two | Ridgeless ensemble · Proportional asymptotics, p/k in (1,∞) and (φ,∞) | GCV risk-estimation error | Hurt |
| Use GCV with a moderate ensemble size (M=5 or 10) to tune subsample size | Ridge ensemble · Single-cell CITE-seq, protein abundance prediction | mean squared error | No clear effect |
Closest papers by meaning
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- Dimension-free deterministic equivalents and scaling laws for random feature regression (NeurIPS 2024 · similarity 0.85)
- Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance (NeurIPS 2024 · similarity 0.84)
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