Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs

1 code implementation • 18 May 2020 • Takashi Goda, Tomohiko Hironaka, Wataru Kitade, Adam Foster

In this paper, applying the idea of randomized multilevel Monte Carlo (MLMC) methods, we introduce an unbiased Monte Carlo estimator for the gradient of the expected information gain with finite expected squared $\ell_2$-norm and finite expected computational cost per sample.

Experimental Design Stochastic Optimization