The Human Kernel
Abstract
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.
Cite
@article{arxiv.1510.07389,
title = {The Human Kernel},
author = {Andrew Gordon Wilson and Christoph Dann and Christopher G. Lucas and Eric P. Xing},
journal= {arXiv preprint arXiv:1510.07389},
year = {2015}
}
Comments
11 pages, 5 figures. To appear in Neural Information Processing Systems (NIPS) 2015. Version 2: Figure 2 (i)-(n) now displays the second set of progressive function learning experiments