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Control variates are variance reduction tools for Monte Carlo estimators.
The conservation of the wild life of Canada
Hewitt, C. G · 1921
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Elements of physical biology
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Variazioni e fluttuazioni del numero d’individui in specie animali conviventi
Volterra, V · 1926
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Fluctuations in the abundance of a species considered mathematically
Lotka, A. J · 1927
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Zero-variance principle for Monte Carlo algorithms
Assaraf, R. and Caffarel, M · 1999
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On the Markov chain central limit theorem
Jones, G. L · 2004
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Learning multiple tasks with kernel methods
Evgeniou, T., Micchelli, C. A., and Pontil, M · 2005
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Control variates for quasi-Monte Carlo
Hickernell, F. J., Lemieux, C., and Owen, A. B · 2005
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On learning vector-valued functions
Micchelli, C. A. and Pontil, M · 2005
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Vector valued reproducing kernel Hilbert spaces of integrable functions and Mercer theorem
Carmeli, C., De Vito, E., and Toigo, A · 2006
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Marginal likelihood estimation via power posteriors
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Estimating Bayes factors via thermodynamic integration and population MCMC
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Vector valued reproducing kernel Hilbert spaces and universality
Carmeli, C., De Vito, E., Toigo, A., and Umanita, V · 2010
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Reproducing Kernel Hilbert Spaces in Probability and Statistics
Berlinet, A. and Thomas-Agnan, C · 2011
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Learning output kernels with block coordinate descent
Dinuzzo, F., Ong, C. S., Gehler, P. V., and Pillonetto, G · 2011
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Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Girolami, M. and Calderhead, B · 2011
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Kernels for vector-valued functions: A review
Álvarez, M. A., Rosasco, L., and Lawrence, N. D · 2012
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Control variates for estimation based on reversible Markov chain Monte Carlo samplers
Dellaportas, P. and Kontoyiannis, I · 2012
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Zero variance Markov chain Monte Carlo for Bayesian estimators
Mira, A., Solgi, R., and Imparato, D · 2013
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Sequential design and analysis of high-accuracy and low-accuracy computer codes
Xiong, S., Qian, P. Z. G., and Wu, C. F. J · 2013
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Stan: A probabilistic programming language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., and Riddell, A · 2017
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Control functionals for Monte Carlo integration
Oates, C. J., Girolami, M., and Chopin, N · 2017
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Remarks on multi-fidelity surrogates
Park, C., Haftka, R. T., and Kim, N. H · 2017
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J · 2018
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On the Poisson equation for Metropolis-Hastings chains
Mijatovic, A. and Vogrinc, J · 2018
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Survey of multifidelity methods in uncertainty propagation, inference, and optimization
Peherstorfer, B., Willcox, K., and Gunzburger, M · 2018
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Improving power posterior estimation of statistical evidence
Friel, N., Hurn, M., and Wyse, J · 2014
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Zero variance differential geometric Markov chain Monte Carlo algorithms
Papamarkou, T., Mira, A., and Girolami, M · 2014
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Convex learning of multiple tasks and their structure
Ciliberto, C., Mroueh, Y., Poggio, T., and Rosasco, L · 2015
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Bayesian computation: a summary of the current state, and samples backwards and forwards
Green, P., Latuszyski, K., Pereyra, M., and Robert, C · 2015
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Adam: A method for stochastic optimization
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Gaussian process optimisation with multi-fidelity evaluations
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Bayesian quadrature for multiple related integrals
Xi, X., Briol, F.-X., and Girolami, M · 2018
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Convergence rates for a class of estimators based on Stein’s method
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Neural control variates for variance reduction
Wan, R., Zhong, M., Xiong, H., and Zhu, Z · 2019
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A unifying and canonical description of measure-preserving diffusions
Barp, A., Takao, S., Betancourt, M., Arnaudon, A., and Girolami, M · 2021
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Control variate selection for monte carlo integration
Leluc, R., Portier, F., and Segers, J · 2021
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Scalable control variates for Monte Carlo methods via stochastic optimization
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Stein’s method meets Statistics: A review of some recent developments
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Meta-learning control variates: Variance reduction with limited data
Sun, Z., Oates, C. J., and Briol, F.-X · 2023
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