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Found 5 papers, 2 papers with code
On the Parallel Complexity of Multilevel Monte Carlo in Stochastic Gradient Descent
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.
A Convex Framework for Confounding Robust Inference
We study policy evaluation of offline contextual bandits subject to unobserved confounders.
Kernel Conditional Moment Constraints for Confounding Robust Inference
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.
Efficient Debiased Evidence Estimation by Multilevel Monte Carlo Sampling
In this paper, we propose a new stochastic optimization algorithm for Bayesian inference based on multilevel Monte Carlo (MLMC) methods.
Multilevel Monte Carlo estimation of log marginal likelihood
In this short note we provide an unbiased multilevel Monte Carlo estimator of the log marginal likelihood and discuss its application to variational Bayes.
