The Human Kernel

Andrew Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity. However, automating human expertise remains elusive; for example, Gaussian processes with standard kernels struggle on function extrapolation problems that are trivial for human learners. In this paper, we create function extrapolation problems and acquire human responses, and then design a kernel learning framework to reverse engineer the inductive biases of human learners across a set of behavioral experiments. We use the learned kernels to gain psychological insights and to extrapolate in human-like ways that go beyond traditional stationary and polynomial kernels. Finally, we investigate Occam's razor in human and Gaussian process based function learning.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 28 (NIPS 2015)
Place of PublicationPalais des Congrès de Montréal, Montréal, CANADA
PublisherNeural Information Processing Systems
Number of pages9
Publication statusPublished - 12 Dec 2015
EventTwenty-ninth Conference on Neural Information Processing Systems - Montreal, Canada
Duration: 7 Dec 201512 Dec 2015

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Electronic)1049-5258


ConferenceTwenty-ninth Conference on Neural Information Processing Systems
Abbreviated titleNIPS 2015
Internet address

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