882 citations · 2.6k across the 89 of their papers we have counts for
6 papers · 2 filters
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations
J. N. Hendriks, C. Jidling, A. Wills +1
This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a…
Automated learning with a probabilistic programming language: Birch
Lawrence M. Murray, Thomas B. Schön
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Tur…
Learning convex bounds for linear quadratic control policy synthesis
Jack Umenberger, Thomas B. Schön
Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics,…
Stochastic quasi-Newton with adaptive step lengths for large-scale problems
Adrian Wills, Thomas Schön
We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxilia…
Conditionally Independent Multiresolution Gaussian Processes
Jalil Taghia, Thomas B. Schön
The multiresolution Gaussian process (GP) has gained increasing attention as a viable approach towards improving the quality of approximations in GPs that scale well to large-scale…
Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama, Dave Zachariah, Thomas B. Schön
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the trainin…