Asymptotic optimality of adaptive importance sampling

Author name not available (Why is that?)

Publication date: 4 June 2018

Abstract: Adaptive importance sampling (AIS) uses past samples to update the extit{sampling policy}

q_{t}

at each stage

t

. Each stage

t

is formed with two steps : (i) to explore the space with

n_{t}

points according to

q_{t}

and (ii) to exploit the current amount of information to update the sampling policy. The very fundamental question raised in this paper concerns the behavior of empirical sums based on AIS. Without making any assumption on the allocation policy

n_{t}

, the theory developed involves no restriction on the split of computational resources between the explore (i) and the exploit (ii) step. It is shown that AIS is asymptotically optimal : the asymptotic behavior of AIS is the same as some "oracle" strategy that knows the targeted sampling policy from the beginning. From a practical perspective, weighted AIS is introduced, a new method that allows to forget poor samples from early stages.

Has companion code repository: https://github.com/portierf/AIS

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