Multi-fidelity Gaussian Process Bandit Optimisation
نویسندگان
چکیده
منابع مشابه
Multi-fidelity Gaussian Process Bandit Optimisation
In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function f . Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer s...
متن کاملGaussian Process Bandit Optimisation with Multi-fidelity Evaluations
In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function f . Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer si...
متن کاملMulti-fidelity Bandit Optimisation∗
In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function. Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations may be available. For example, in optimal policy search in robotics, the expensive real world behaviour of a robot can be approx...
متن کاملGaussian Process Optimisation with Multi-fidelity Evaluations
In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function f . Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer si...
متن کاملThe Multi-fidelity Multi-armed Bandit
We study a variant of the classical stochastic K-armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available. For example, in online advertising the performance of an ad can be approximated by displaying it for shorter time periods or to narrower audiences. We formalise this task as a multi-fidelity bandit, where, at each time step, ...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2019
ISSN: 1076-9757
DOI: 10.1613/jair.1.11288