Simulation

By F. Bastin, Fabian Bastin Ift. 2006

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning (2017, methodology): “The control variates method (Ross, 2006) is a general technique for variance reduction of a Monte Carlo estimator by exploiting a correlated variable for which we know more information such as analytical expectation.”
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning (2017, methodology): “In the following, we explain how antithetic and additive control variate techniques from Monte Carlo simulation (Ross, 2013) can be used to remedy these problems.”
Underwater Optical Wireless Communications, Networking, and Localization: A Survey (2018, background): “The Gaussian light-beam propagation in turbulent sea water was studied in [98, 99, 100].”
Counterfactual Learning for Machine Translation: Degeneracies and Solutions (2017, methodology): “Let δ̂(xt, yt) be a regression-based reward model trained on the logged data, and let ĉ be a scalar that allows to optimize the estimator for minimal variance (Ross, 2013).”
Advances in Variational Inference (2017, methodology): “Using control variates for variance reduction is common in Monte Carlo simulation and stochastic optimization [146], [184].”