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Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated.
Probability inequalities for sums of bounded random variables
W. Hoeffding · 1963
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Monte carlo is fundamentally unsound
A. O’Hagan · 1987
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On control variate estimators
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U-statistics: Theory and Practice
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Bayes–Hermite quadrature
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Better subset regression using the nonnegative garrote
L. Breiman · 1995
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The Ising/Potts model is not well suited to segmentation tasks
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Sequential importance sampling for nonparametric Bayes models: The next generation
S. N. MacEachern, M. Clyde, and J. S. Liu · 1999
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Annealed importance sampling
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A parallel mixture of SVMs for very large scale problems
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Variance reduction techniques for gradient estimates in reinforcement learning
E. Greensmith, P. L. Bartlett, and J. Baxter · 2004
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Importance sampling via the estimated sampler
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Direct importance estimation with model selection and its application to covariate shift adaptation
M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe · 2008
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Graphical models, exponential families, and variational inference
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Monte Carlo strategies in scientific computing
J. S. Liu · 2008
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Super-samples from kernel herding
Y. Chen, M. Welling, and A. Smola · 2010
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Bayesian learning via stochastic gradient Langevin dynamics
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Machine learning in non-stationary environments: Introduction to covariate shift adaptation
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Integral approximation by kernel smoothing
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Black box variational inference
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
M. D. Hoffman and A. Gelman · 2014
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Variational inference with normalizing flows
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Probabilistic variational bounds for graphical models
Q. Liu, J. W. Fisher III, and A. T. Ihler · 2015
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Optimally-weighted Herding is Bayesian quadrature
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Markov chain importance sampling with applications to rare event probability estimation
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Initializing adaptive importance sampling with markov chains
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Stochastic optimization with importance sampling
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