Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Active Learning of Linear Embeddings for Gaussian Processes
Roman Garnett, Michael Osborne, Philipp Hennig
Abstract:
We propose an active learning method for discovering low-dimensional structure in high- dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and impor- tant, but have hitherto presented severe practical difficulties. We further introduce a novel tech- nique for approximately marginalizing GP hyper- parameters, yielding marginal predictions robust to hyperparameter misspecification. Our method offers an efficient means of performing GP re- gression, quadrature, or Bayesian optimization in high-dimensional spaces.
Keywords:
Pages: 230-239
PS Link:
PDF Link: /papers/14/p230-garnett.pdf
BibTex:
@INPROCEEDINGS{Garnett14,
AUTHOR = "Roman Garnett and Michael Osborne and Philipp Hennig",
TITLE = "Active Learning of Linear Embeddings for Gaussian Processes",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "230--239"
}


hosted by DSL   •   site info   •   help