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Control variates are a well-established tool to reduce the variance of Monte Carlo estimators.
Variance reduction for MCMC methods via martingale representations
Belomestny, D., Moulines, E., Shagadatov, N., and Urusov, M. (2019) · 1903
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On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case
Chau, N. H., Moulines, E., Rásonyi, M., Sabanis, S., and Zhang, Y. (2019) · 1905
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Control variate selection for Monte Carlo integration
Leluc, R., Portier, F., and Segers, J. (2019) · 1906
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Zhang, Y., Akyildiz, O. D., Damoulas, T., and Sabanis, S. (2019) · 1910
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Principles of physical biology
Lotka, A. J. (1925) · 1925
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Fluctuations in the abundance of a species considered mathematically
Volterra, V. (1926) · 1926
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Adaptive switching circuits
Widrow, B. and Hoff, M. E. (1960) · 1960
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A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Stein, C. (1972) · 1972
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Testing multidimensional integration routines
Genz, A. (1984) · 1984
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Stein’s method and Poisson process convergence
Barbour, A. D. (1988) · 1988
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Analysis of hidden units in a layered network trained to classify sonar targets
Gorman, R. P. and Sejnowski, T. J. (1988) · 1988
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Bayes-Hermite quadrature
O’Hagan, A. (1991) · 1991
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Acceleration of stochastic approximation by averaging
Polyak, B. T. and Juditsky, A. B. (1992) · 1992
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Variance reduction through smoothing and control variates for Markov chain simulations
Andradóttir, S., Heyman, D. P., and Ott, T. J. (1993) · 1993
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Variance reduction for simulated diffusions
Newton, N. J. (1994) · 1994
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Exponential convergence of Langevin distributions and their discrete approximations
Roberts, G. O. and Tweedie, R. L. (1996) · 1996
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Zero-variance principle for Monte Carlo algorithms
Assaraf, R. and Caffarel, M. (1999) · 1999
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Bayesian calibration of computer models
Kennedy, M. C. and Hagan, A. O. (2001) · 2001
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On the Poisson equation and diffusion approximation. I
Pardoux, E. and Vertennikov, A. Y. (2001) · 2001
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Approximating martingales for variance reduction in Markov process simulation
Henderson, S. G. and Glynn, P. W. (2002) · 2002
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Semi-exact control functionals from Sard’s method
South, L. F., Karvonen, T., Nemeth, C., Girolami, M., and Oates, C. J. (2020) · 2002
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Design of experiments for the NIPS 2003 variable selection benchmark
Guyon, I. (2003) · 2003
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Semiparametric regression
Ruppert, D., Wand, M. P., and Carroll, R. J. (2003) · 2003
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Variance reduction techniques for gradient estimates in reinforcement learning
Greensmith, E., Bartlett, P. L., and Baxter, J. (2004) · 2004
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Solving large scale linear prediction problems using stochastic gradient descent algorithms
Zhang, T. (2004) · 2004
Cited alongside, same era.
Control variates for quasi-Monte Carlo
Hickernell, F. J., Lemieux, C., and Owen, A. B. (2005) · 2005
Cited alongside, same era.
Optimal thinning of MCMC output
Riabiz, M., Chen, W., Cockayne, J., Swietach, P., Niederer, S. A., Mackey, L., and Oates, C. J. (2020) · 2005
Cited alongside, same era.
Gaussian Processes for Machine Learning
Rasmussen, C. and Williams, C. (2006) · 2006
Cited alongside, same era.
Control variates for the Metropolis-Hastings algorithm
Hammer, H. and Tjelmeland, H. (2008) · 2008
Cited alongside, same era.
Normal Approximation by Stein’s Method
Chen, L. H. Y., Goldstein, L., and Shao, Q.-M. (2010) · 2010
Black-box importance sampling
Liu, Q. and Lee, J. D. (2017) · 2017
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Control functionals for Monte Carlo integration
Oates, C. J., Girolami, M., and Chopin, N. (2017) · 2017
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Non-convex learning via stochastic gradient Langevin dynamics: a non-asymptotic analysis
Raginsky, M., Rakhlin, A., and Telgarsky, M. (2017) · 2017
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Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
Tucker, G., Mnih, A., Maddison, C. J., Lawson, J., and Sohl-Dickstein, J. (2017) · 2017
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A Riemannian-Stein kernel method
Barp, A., Oates, C. J., Porcu, E., and Girolami, M. (2018) · 2018
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Variance reduction via empirical variance minimization: convergence and complexity
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Cited alongside, same era.
Fundamentals of Stein’s method
Ross, N. (2011) · 2011
Cited alongside, same era.
Control variates for estimation based on reversible Markov chain Monte Carlo samplers
Dellaportas, P. and Kontoyiannis, I. (2012) · 2012
Cited alongside, same era.
Variational Bayesian inference with stochastic search
Paisley, J., Blei, D., and Jordan, M. (2012) · 2012
Cited alongside, same era.
Zero variance Markov chain Monte Carlo for Bayesian estimators
Mira, A., Solgi, R., and Imparato, D. (2013) · 2013
Cited alongside, same era.
Variance reduction for stochastic gradient optimization
Wang, C., Chen, X., Smola, A., and Xing, E. P. (2013) · 2013
Cited alongside, same era.
Exploiting multi-core architectures for reduced-variance estimation with intractable likelihoods
Friel, N., Mira, A., and Oates, C. J. (2014) · 2014
Cited alongside, same era.
Belomestny, D., Iosipoi, L., and Zhivotovskiy, N. (2018) · 2018
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J. (2018) · 2018
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Diffusion approximations and control variates for MCMC
Brosse, N., Durmus, A., Meyn, S., and Moulines, E. (2018) · 2018
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Stein points
Chen, W. Y., Mackey, L., Gorham, J., Briol, F.-X., and Oates, C. J. (2018) · 2018
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Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Grathwohl, W., Choi, D., Wu, Y., Roeder, G., and Duvenaud, D. (2018) · 2018
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Action-dependent control variates for policy optimization via Stein’s identity
Liu, H., Feng, Y., Mao, Y., Zhou, D., Peng, J., and Liu, Q. (2018) · 2018
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Goodness-of-fit testing for discrete distributions via Stein discrepancy
Yang, J., Liu, Q., Rao, V., and Neville, J. (2018) · 2018
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Control variates for stochastic gradient MCMC
Baker, J., Fearnhead, P., Fox, E. B., and Nemeth, C. (2019) · 2019
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Minimum Stein discrepancy estimators
Barp, A., Briol, F.-X., Duncan, A. B., Girolami, M., and Mackey, L. (2019) · 2019
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Probabilistic integration: A role in statistical computation? (with discussion)
Briol, F.-X., Oates, C. J., Girolami, M., Osborne, M. A., and Sejdinovic, D. (2019) · 2019
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Stein point Markov chain Monte Carlo
Chen, W. Y., Barp, A., Briol, F.-X., Gorham, J., Girolami, M., Mackey, L., and Oates, C. J. (2019) · 2019
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Measuring sample quality with diffusions
Gorham, J., Duncan, A., Mackey, L., and Vollmer, S. (2019) · 2019
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Fisher efficient inference of intractable models
Liu, S., Kanamori, T., Jitkrittum, W., and Chen, Y. (2019) · 2019
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Convergence rates for a class of estimators based on Stein’s identity
Oates, C. J., Cockayne, J., Briol, F.-X., and Girolami, M. (2019) · 2019
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Monte Carlo integration with a growing number of control variates
Portier, F. and Segers, J. (2019) · 2019
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Regularised zero-variance control variates for high-dimensional variance reduction
South, L. F., Oates, C. J., Mira, A., and Drovandi, C. (2019) · 2019
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Neural control variates for monte carlo variance reduction
Wan, R., Zhong, M., Xiong, H., and Zhu, Z. (2019) · 2019
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Variance reduction for Markov chains with application to MCMC
Belomestny, D., Iosipoi, L., Moulines, E., Naumov, A., and Samsonov, S. (2020) · 2020
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Stein’s method meets statistics: A review of some recent developments
Anastasiou, A., Barp, A., Briol, F.-X., Ebner, B., Gaunt, R. E., Ghaderinezhad, F., Gorham, J., Gretton, A., Ley, C., Liu, Q., Mackey, L., Oates, C. J., Reinert, G., and Swan, Y. (2021) · 2021
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