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Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD) has received much interest recently.
Introductory functional analysis with applications , volume 1
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Set-Valued Analysis
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Independent component analysis, a new concept?
Comon, P · 1994
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A new learning algorithm for blind signal separation
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Blind signal separation: statistical principles
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The variational formulation of the Fokker-Planck equation
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Generalization of an inequality by Talagrand and links with the logarithmic Sobolev inequality
Otto, F. and Villani, C · 2000
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The geometry of dissipative evolution equations: the porous medium equation
Otto, F · 2001
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Optimal transportation, dissipative PDE’s and functional inequalities
Villani, C · 2003
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P · 2005
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An introduction to partial differential equations , volume 13
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Matplotlib: A 2d graphics environment
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Gradient flows: in metric spaces and in the space of probability measures
Ambrosio, L., Gigli, N., and Savaré, G · 2008
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Support vector machines
Steinwart, I. and Christmann, A · 2008
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Functional analysis, Sobolev spaces and partial differential equations
Brezis, H · 2010
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Universality, characteristic kernels and RKHS embedding of measures
Sriperumbudur, B. K., Fukumizu, K., and Lanckriet, G. R · 2011
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Randomized smoothing for stochastic optimization
Duchi, J. C., Bartlett, P. L., and Wainwright, M. J · 2012
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Nonparametric variational inference
Gershman, S., Hoffman, M., and Blei, D · 2012
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Analysis and geometry of Markov diffusion operators , volume 348
Bakry, D., Gentil, I., and Ledoux, M · 2013
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Probability theory: a comprehensive course
Klenke, A · 2013
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Continuity equations and ODE flows with non-smooth velocity
Ambrosio, L. and Crippa, G · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Introduction to the spectral theory
Pankrashkin, K · 2014
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On the geometry of Stein variational gradient descent
Duncan, A., Nüsken, N., and Szpruch, L · 2019
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Analysis of Langevin Monte Carlo via convex optimization
Durmus, A., Majewski, S., and Miasojedow, B · 2019
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Bayesian posterior approximation via greedy particle optimization
Futami, F., Cui, Z., Sato, I., and Sugiyama, M · 2019
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Sobolev descent
Mroueh, Y., Sercu, T., and Raj, A · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Stochastic processes and applications: diffusion processes, the Fokker-Planck and Langevin equations , volume 60
Pavliotis, G. A · 2014
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A kernel test of goodness of fit
Chwialkowski, K., Strathmann, H., and Gretton, A · 2016
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Scaling-up empirical risk minimization: optimization of incomplete u-statistics
Clémençon, S., Colin, I., and Bellet, A · 2016
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Reflection couplings and contraction rates for diffusions
Eberle, A · 2016
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
Liu, Q. and Wang, D · 2016
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A kernelized Stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J., and Jordan, M · 2016
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Chewi, S., Gouic, T. L., Lu, C., Maunu, T., and Rigollet, P · 2020
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Array programming with NumPy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del R’ıo, J. F., Wiebe, M., Peterson, P., G’erard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E · 2020
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The reproducing Stein kernel approach for post-hoc corrected sampling
Hodgkinson, L., Salomone, R., and Roosta, F · 2020
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A kernel Stein test for comparing latent variable models
Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., and Gretton, A · 2020
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A non-asymptotic analysis for Stein variational gradient descent
Korba, A., Salim, A., Arbel, M., Luise, G., and Gretton, A · 2020
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Optimal thinning of MCMC output
Riabiz, M., Chen, W., Cockayne, J., Swietach, P., Niederer, S. A., Mackey, L., Oates, C., et al · 2020
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Wasserstein proximal gradient
Salim, A., Korba, A., and Luise, G · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors · 2020
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Blindness of score-based methods to isolated components and mixing proportions
Wenliang, L. K · 2020
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A Stein goodness-of-fit test for directional distributions
Xu, W. and Matsuda, T · 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
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Differential inclusions in Wasserstein spaces: The Cauchy-Lipschitz framework
Bonnet, B. and Frankowska, H · 2021
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Measure transport with kernel Stein discrepancy
Fisher, M. A., Nolan, T., Graham, M. M., Prangle, D., and Oates, C. J · 2021
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