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This paper is concerned with the theoretical and computational development of a new class of nonlinear filtering algorithms called the optimal transport particle filters (OTPF).
Topics in propagation of chaos
Alain-Sol Sznitman · 1991
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Asymptotic stability of the optimal filter with respect to its initial condition
Daniel Ocone and Etienne Pardoux · 1996
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Exponential stability for nonlinear filtering
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Martin Anthony and Peter L Bartlett · 1999
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On the stability of interacting processes with applications to filtering and genetic algorithms
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Data Assimilation: The Ensemble Kalman Filter
Geir Evensen · 2006
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Inference in hidden markov models
Olivier Cappé, Eric Moulines, and Tobias Rydén · 2009
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Intrinsic methods in filter stability
Pavel Chigansky, Robert Liptser, and Ramon Van Handel · 2009
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A tutorial on particle filtering and smoothing: Fifteen years later
Arnaud Doucet and Adam M Johansen · 2009
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Observability and nonlinear filtering
Ramon Van Handel · 2009
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Uniform observability of hidden Markov models and filter stability for unstable signals
Ramon Van Handel · 2009
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Optimal Transport: Old and New
Cédric Villani · 2009
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Texture mapping via optimal mass transport
Ayelet Dominitz and Allen Tannenbaum · 2010
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Nonlinear filtering and systems theory
Ramon Van Handel · 2010
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The Oxford handbook of nonlinear filtering
Dan Crisan and Boris Rozovskii · 2011
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Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2011
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Bayesian inference with optimal maps
Tarek A El Moselhy and Youssef M Marzouk · 2012
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Markov chains and stochastic stability
Sean P Meyn and Richard L Tweedie · 2012
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Yuan Cheng and Sebastian Reich · 2013
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A nonparametric ensemble transform method for Bayesian inference
Sebastian Reich · 2013
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Feedback particle filter
Tao Yang, Prashant G Mehta, and Sean P Meyn · 2013
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Regularized discrete optimal transport
Sira Ferradans, Nicolas Papadakis, Gabriel Peyré, and Jean-François Aujol · 2014
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Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
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Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2017
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On the discrimination-generalization tradeoff in GANs
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He · 2017
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Minimax rates of estimation for smooth optimal transport maps
Jan-Christian Hütter and Philippe Rigollet · 2019
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Data assimilation: The Schrödinger perspective
Sebastian Reich · 2019
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Brandon Amos, Lei Xu, and J Zico Kolter · 2016
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Coupling techniques for nonlinear ensemble filtering
Alessio Spantini, Ricardo Baptista, and Youssef Marzouk · 2019
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Scalable computations of Wasserstein barycenter via input convex neural networks
Jiaojiao Fan, Amirhossein Taghvaei, and Yongxin Chen · 2020
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Conditional sampling with monotone GANs
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Optimal transport mapping via input convex neural networks
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An optimal transport formulation of the ensemble Kalman filter
Amirhossein Taghvaei and Prashant G Mehta · 2020
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Variational Wasserstein gradient flow
Jiaojiao Fan, Amirhossein Taghvaei, and Yongxin Chen · 2021
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Preconditioned training of normalizing flows for variational inference in inverse problems
Ali Siahkoohi, Gabrio Rizzuti, Mathias Louboutin, Philipp A Witte, and Felix J Herrmann · 2021
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Optimal transportation methods in nonlinear filtering: The feedback particle filter
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Ensemble Kalman methods: a mean field perspective
Edoardo Calvello, Sebastian Reich, and Andrew M Stuart · 2022
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Duality for nonlinear filtering i: Observability
Jin Won Kim and Prashant G Mehta · 2022
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Deep Ray, Harisankar Ramaswamy, Dhruv V Patel, and Assad A Oberai · 2022
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Conditional simulation using diffusion Schrödinger bridges
Yuyang Shi, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet · 2022
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An optimal transport formulation of Bayes’ law for nonlinear filtering algorithms
Amirhossein Taghvaei and Bamdad Hosseini · 2022
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