Mitigating the noise of DESI mocks using analytic control variates [PDF]
The Open Journal of Astrophysics, 2023 In order to address fundamental questions related to the expansion history of the Universe and its primordial nature with the next generation of galaxy experiments, we need to model reliably large-scale structure observables such as the correlation ...
Boryana Hadzhiyska +32 more
doaj +5 more sources
ACM Transactions on Graphics, 2020 We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variates has been finding a good approximation of the integrand that is cheap to integrate.
Fabrice Rousselle +2 more
exaly +4 more sources
Efficient Attention via Control Variates [PDF]
International Conference on Learning Representations, 2023 Random-feature-based attention (RFA) is an efficient approximation of softmax attention with linear runtime and space complexity. However, the approximation gap between RFA and conventional softmax attention is not well studied.
Lin Zheng +3 more
semanticscholar +2 more sources
Meta-learning Control Variates: Variance Reduction with Limited Data [PDF]
Conference on Uncertainty in Artificial Intelligence, 2023 Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples is small.
Z. Sun, C. Oates, F. Briol
semanticscholar +4 more sources
Asymptotic expansions as control variates for deep solvers to fully-coupled forward-backward stochastic differential equations. [PDF]
PLoS ONECoupled forward-backward stochastic differential equations (FBSDEs) are closely related to financially important issues such as optimal investment. However, it is well known that obtaining solutions is challenging, even when employing numerical methods ...
Makoto Naito +3 more
doaj +2 more sources
Control variates for stochastic gradient MCMC [PDF]
Statistics and Computing, 2018 It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC. These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.
Jack Baker +3 more
openaire +6 more sources
Implementation of variance reduction techniques applied to the pricing of investment certificates [PDF]
Risk Management Magazine, 2023 Certificates are structured financial instruments that aim to provide investors with investment solutions tailored to their needs. Certificates can be modeled using a bond component and a derivative component, typically an options strategy.
Anna Bottasso +3 more
doaj +1 more source
Stabilizing Estimates of Shapley Values with Control Variates [PDF]
xAI, 2023 Shapley values are among the most popular tools for explaining predictions of blackbox machine learning models. However, their high computational cost motivates the use of sampling approximations, inducing a considerable degree of uncertainty.
Jeremy Goldwasser, Giles Hooker
semanticscholar +1 more source
Modified control variates method based on second-order saddle-point approximation for practical reliability analysis [PDF]
Mechanical Sciences, 2023 A novel method is presented for efficiently analyzing the reliability of engineering components and systems with highly nonlinear complex limit state functions.
X. En, X. En, Y. Zhang, X. Huang
doaj +1 more source
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates [PDF]
SIAM/ASA J. Uncertain. Quantification, 2023 We provide a collection of results on covariance expressions between Monte Carlo based multi-output mean, variance, and Sobol main effect variance estimators from an ensemble of models.
Thomas O. Dixon +3 more
semanticscholar +1 more source