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Efficient High-Dimensional Stochastic Optimal Motion Control using Tensor-Train Decomposition., , and . Robotics: Science and Systems, (2015)Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction., , and . J. Comput. Physics, (2016)Data-free inference of the joint distribution of uncertain model parameters., , , , and . J. Comput. Physics, 231 (5): 2180-2198 (2012)Dimension-independent likelihood-informed MCMC., , and . J. Comput. Physics, (2016)Efficient Localization of Discontinuities in Complex Computational Simulations., and . CoRR, (2014)Inference via Low-Dimensional Couplings., , and . J. Mach. Learn. Res., (2018)Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems., , , , and . SIAM J. Scientific Computing, (2017)Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation., and . SIAM/ASA J. Uncertain. Quantification, 4 (1): 796-828 (2016)Localization for MCMC: sampling high-dimensional posterior distributions with local structure., , and . J. Comput. Physics, (2019)A Stein variational Newton method., , , , and . NeurIPS, page 9187-9197. (2018)