Combining benefits of kernels with Bayesian models, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also quantifying the associated uncertainty. While most GP approaches rely on a single preselected prior, the present work employs a weighted ensemble of GP priors, each having a unique covariance (kernel) belonging to a prescribed kernel dictionary - which leads to a richer space of learning functions. Leveraging kernel approximants formed by spectral features for scalability, an online interactive ensemble (OI-E) GP framework is developed to jointly learn the sought function, and for the first time select interactively the EGP kernel on-the-fly. Performance of OI-EGP is benchmarked by the best fixed function estimator via regret analysis. Furthermore, the novel OI-EGP is adapted to accommodate dynamic learning functions. Synthetic and real data tests demonstrate the effectiveness of the proposed schemes.
|Original language||English (US)|
|Number of pages||11|
|Journal||Proceedings of Machine Learning Research|
|State||Published - 2020|
|Event||23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 - Virtual, Online|
Duration: Aug 26 2020 → Aug 28 2020
Bibliographical noteFunding Information:
Acknowledgement. We would like to thank the anonymous reviewers for their constructive feedback. We also gratefully acknowledge the support from NSF grants 1508993, 1711471 and 1901134.
Copyright © 2020 by the author(s)