no code implementations • 3 Oct 2023 • Kei Ishikawa
In the stochastic gradient descent (SGD) for sequential simulations such as the neural stochastic differential equations, the Multilevel Monte Carlo (MLMC) method is known to offer better theoretical computational complexity compared to the naive Monte Carlo approach.
1 code implementation • 21 Sep 2023 • Kei Ishikawa, Niao He, Takafumi Kanamori
We study policy evaluation of offline contextual bandits subject to unobserved confounders.
2 code implementations • 26 Feb 2023 • Kei Ishikawa, Niao He
It can be shown that our estimator contains the recently proposed sharp estimator by Dorn and Guo (2022) as a special case, and our method enables a novel extension of the classical marginal sensitivity model using f-divergence.
no code implementations • 14 Jan 2020 • Kei Ishikawa, Takashi Goda
In this paper, we propose a new stochastic optimization algorithm for Bayesian inference based on multilevel Monte Carlo (MLMC) methods.
no code implementations • 23 Dec 2019 • Takashi Goda, Kei Ishikawa
In this short note we provide an unbiased multilevel Monte Carlo estimator of the log marginal likelihood and discuss its application to variational Bayes.