Research map / Learning theory and algorithms / Kernels and regression theory
Hypothesis Transfer Learning via Transformation Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use an offset transformation instead of target-only KRR | Kernel Smoothing · kin-8fm → kin-8nh, n_ta = 40 | mean squared error: 0.063 ± 0.013 -> 0.052 ± 0.006 | Helped |
| Use an offset transformation instead of target-only KRR | Kernel Ridge Regression · kin-8fm → kin-8nh, n_ta = 80 | mean squared error: 0.050 ± 0.007 -> 0.048 ± 0.006 | Helped |
| Use an offset transformation instead of target-only KRR | Kernel Ridge Regression · kin-8fm → kin-8nh, n_ta = 320 | mean squared error: 0.040 ± 0.005 -> 0.041 ± 0.003 | Hurt |
| Use an offset transformation rather than a scale transformation | Transfer learning · kin-8fm → kin-8nh | mean squared error | Mixed |
| Use an offset transformation instead of target-only KRR | Kernel Smoothing · fMRI Stroop response-time prediction | R-squared (mean): 0.1041 -> 0.1190 | Helped |
| Use an offset transformation rather than a scale transformation | Transfer learning · fMRI Stroop response-time prediction | R-squared (mean): 0.0017 -> 0.1190 | Helped |
| Use an offset transformation rather than a scale transformation | Transfer learning · fMRI Stroop response-time prediction | R-squared standard deviation: 0.0632; 0.1104 | Mixed |
| Choose among transformation functions by cross-validation, including a no-transfer option | Transformation-function selection · Target-domain regression | excess risk | No clear effect |
| Use a transformation when it makes the auxiliary function smoother than the target regression function | Kernel Smoothing HTL · Target-domain regression | excess risk convergence rate | Helped |
| Use transfer learning when the auxiliary function has at least the target function's approximation regularity and source data is sufficiently large | Kernel Ridge Regression HTL · Target-domain regression | excess risk convergence rate | Helped |
Closest papers by meaning
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- Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression (NeurIPS 2025 · similarity 0.86)
- Transfer Learning of Linear Regression with Multiple Pretrained Models: Benefiting from More Pretrained Models via Overparameterization Debiasing (NeurIPS 2026 · similarity 0.85)
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