Gaussian process bandits with adaptive discretization
نویسندگان
چکیده
منابع مشابه
Gaussian Process bandits with adaptive discretization
In this paper, the problem of maximizing a black-box function f : X → R is studied in the Bayesian framework with a Gaussian Process (GP) prior. In particular, a new algorithm for this problem is proposed, and high probability bounds on its simple and cumulative regret are established. The query point selection rule in most existing methods involves an exhaustive search over an increasingly fin...
متن کاملHigh-Dimensional Gaussian Process Bandits
Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from noisy samples that are expensive to obtain. We address this notoriously hard challenge, under the assumptions that the function varies only along some low-dimensional subspace and is smooth (i.e., it has a low norm in a Reproducible Kernel Hilbert Space). In particular, we prese...
متن کاملRegret Bounds for Deterministic Gaussian Process Bandits
This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al., 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, (Srinivas et al., 2010) proved that the regret vanishes at the approximate rate of O ( 1 √ t ) , where t...
متن کاملGaussian Process Bandits: An Experimental Design Approach
We consider the online setting of optimizing an unknown function, sampled from a Gaussian Proccess, over a given bounded decision set so that that our cumulative regret is low. Our analysis of an upper confidence algorithm provides sublinear regret bounds for popular classes of kernels by exploiting a surprising connection to optimal experimental design– in particular, our rates do no explicitl...
متن کاملExponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
This paper analyzes the problem of Gaussian process (GP) bandits with deterministic observations. The analysis uses a branch and bound algorithm that is related to the UCB algorithm of (Srinivas et al., 2010). For GPs with Gaussian observation noise, with variance strictly greater than zero, (Srinivas et al., 2010) proved that the regret vanishes at the approximate rate of O ( 1 √ t ) , where t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2018
ISSN: 1935-7524
DOI: 10.1214/18-ejs1497