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Hypothesis Transfer Learning via Transformation Functions

NeurIPS 2017 · Learning theory and algorithms › Kernels and regression theory · approach: High-dimensional asymptotic generalization · 36 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use an offset transformation instead of target-only KRRKernel Smoothing · kin-8fm → kin-8nh, n_ta = 40mean squared error: 0.063 ± 0.013 -> 0.052 ± 0.006Helped
Use an offset transformation instead of target-only KRRKernel Ridge Regression · kin-8fm → kin-8nh, n_ta = 80mean squared error: 0.050 ± 0.007 -> 0.048 ± 0.006Helped
Use an offset transformation instead of target-only KRRKernel Ridge Regression · kin-8fm → kin-8nh, n_ta = 320mean squared error: 0.040 ± 0.005 -> 0.041 ± 0.003Hurt
Use an offset transformation rather than a scale transformationTransfer learning · kin-8fm → kin-8nhmean squared errorMixed
Use an offset transformation instead of target-only KRRKernel Smoothing · fMRI Stroop response-time predictionR-squared (mean): 0.1041 -> 0.1190Helped
Use an offset transformation rather than a scale transformationTransfer learning · fMRI Stroop response-time predictionR-squared (mean): 0.0017 -> 0.1190Helped
Use an offset transformation rather than a scale transformationTransfer learning · fMRI Stroop response-time predictionR-squared standard deviation: 0.0632; 0.1104Mixed
Choose among transformation functions by cross-validation, including a no-transfer optionTransformation-function selection · Target-domain regressionexcess riskNo clear effect
Use a transformation when it makes the auxiliary function smoother than the target regression functionKernel Smoothing HTL · Target-domain regressionexcess risk convergence rateHelped
Use transfer learning when the auxiliary function has at least the target function's approximation regularity and source data is sufficiently largeKernel Ridge Regression HTL · Target-domain regressionexcess risk convergence rateHelped

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