Research map / Learning theory and algorithms / Kernels and regression theory
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Ensemble independently trained learners by averaging their output fields | Random-feature learners · Synthetic regression and classification | test error | Helped |
| Use score averaging instead of majority vote | Logistic-regression ensemble · Binary classification | test error | Helped |
| Increase the number of learners | Ensemble predictors · Binary classification | test error | Mixed |
| Increase overparameterisation | Random-feature learners · Binary classification | probability of disagreement | Helped |
| Take the kernel limit, where q1=q0 | Random-feature ensemble · Kernel limit | generalisation error | No clear effect |
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