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Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. While several studies address the problem of what to transfer, the very important question of when to answer remains mostly unanswered, especially from a theoretical point-of-view for regression problems. A new theoretical framework for the problem of parameter transfer for the linear model is proposed. It is shown that the quality of transfer for a new input vector depends on its representation in an eigenbasis involving the parameters of the problem. Furthermore, a statistical test is constructed to predict whether a fine-tuned model has a lower prediction quadratic risk than the base target model for an unobserved sample. Efficiency of the test is illustrated on synthetic data as well as real electricity consumption data. • A new approach of parametric transfer is proposed for the linear model. • The benefits of transfer are quantified using the gain in prediction error. • A statistical test is proposed to predict in advance whether transfer is beneficial or not for a new sample x. • The methodology has been applied on synthetic and real-world data with success. [ABSTRACT FROM AUTHOR]

Focuses on the author's selection of the most overrated and the most underrated counter culture heroes of the 1960s in the United States. Publicity-seeking stunts of Abbie Hoffman and Jerry Rubin considered as the most overrated; Influence of Stewart Brand's contributions to alternative thinking considered as the most underrated.