Research map / Learning theory and algorithms / Kernels and regression theory
Distributionally Robust Optimization and Generalization in Kernel Methods
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the squared RKHS norm penalty with the loss-inspired penalty ||f²||_{σ/√2} (ablation) | Gaussian kernel ridge regression · Synthetic easy and hard settings | population risk | Helped |
| Use the penalty ||f²||_{σ/√2} instead of ||f||²_σ (ablation) | Gaussian kernel ridge regression · Synthetic easy and hard settings | sensitivity to λ | Helped |
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