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The recently developed Particle-based Variational Inference (ParVI) methods drive the empirical distribution of a set of \emph{fixed-weight} particles towards a given target distribution $\pi$ by iteratively updating particles' positions.
Global convergence of neuron birth-death dynamics
Rotskoff, G.; Jelassi, S.; Bruna, J.; and Vanden-Eijnden, E. 2019 · 1902
Earlier work this paper cites.
Accelerating langevin sampling with birth-death
Lu, Y.; Lu, J.; and Nolen, J. 2019 · 1905
Earlier work this paper cites.
Accelerated information gradient flow
Wang, Y.; and Li, W. 2019 · 1909
Earlier work this paper cites.
Information and accuracy attainable in the estimation of statistical parameters. Kotz S & Johnson NL (eds.), Breakthroughs in Statistics Volume I: Foundations and Basic Theory, 235–248
Rao, C. 1945 · 1945
Earlier work this paper cites.
On equivalence of infinite product measures
Kakutani, S. 1948 · 1948
Earlier work this paper cites.
Implicit runge-kutta processes
Butcher, J. C. 1964 · 1964
Earlier work this paper cites.
The variational formulation of the Fokker–Planck equation
Jordan, R.; Kinderlehrer, D.; and Otto, F. 1998 · 1998
Earlier work this paper cites.
Hellinger distance
Nikulin, M. S.; et al. 2001 · 2001
Earlier work this paper cites.
Gaussian processes in machine learning
Rasmussen, C. E. 2003 · 2003
Earlier work this paper cites.
An introduction to numerical analysis
Süli, E.; and Mayers, D. F. 2003 · 2003
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
Ambrosio, L.; Gigli, N.; and Savaré, G. 2008 · 2008
Earlier work this paper cites.
Numerical solution of stochastic differential equations with jumps in finance , volume 64
Platen, E.; and Bruti-Liberati, N. 2010 · 2010
Earlier work this paper cites.
Handbook of markov chain monte carlo
Brooks, S.; Gelman, A.; Jones, G.; and Meng, X.-L. 2011 · 2011
Earlier work this paper cites.
Black box variational inference
Ranganath, R.; Gerrish, S.; and Blei, D. 2014 · 2014
Cited alongside, same era.
A complete recipe for stochastic gradient MCMC
Ma, Y.-A.; Chen, T.; and Fox, E. B. 2015 · 2015
Cited alongside, same era.
A blob method for the aggregation equation
Craig, K.; and Bertozzi, A. 2016 · 2016
Cited alongside, same era.
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Kondratyev, S.; Monsaingeon, L.; Vorotnikov, D.; et al. 2016 · 2016
Cited alongside, same era.
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Cited alongside, same era.
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Liu, Q.; Lee, J.; and Jordan, M. 2016 · 2016
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Liu, Q.; and Wang, D. 2018 · 2018
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Message passing Stein variational gradient descent
Zhuo, J.; Liu, C.; Shi, J.; Zhu, J.; Chen, N.; and Zhang, B. 2018 · 2018
Later among the works it cites.
An unbalanced optimal transport splitting scheme for general advection-reaction-diffusion problems
Gallouët, T.; Laborde, M.; and Monsaingeon, L. 2019 · 2019
Later among the works it cites.
Understanding and accelerating particle-based variational inference
Liu, C.; Zhuo, J.; Cheng, P.; Zhang, R.; and Zhu, J. 2019 · 2019
Later among the works it cites.
Understanding mcmc dynamics as flows on the wasserstein space
Liu, C.; Zhuo, J.; and Zhu, J. 2019 · 2019
Later among the works it cites.
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Liu, Q.; and Wang, D. 2016 · 2016
Cited alongside, same era.
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Chen, C.; and Zhang, R. 2017 · 2017
Cited alongside, same era.
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Gallouët, T. O.; and Monsaingeon, L. 2017 · 2017
Cited alongside, same era.
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Mroueh, Y.; and Rigotti, M. 2020 · 2020
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Zhang, J.; Zhao, Y.; and Chen, C. 2020 · 2020
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Zhu, M.; Liu, C.; and Zhu, J. 2020 · 2020
Later among the works it cites.
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Flamary, R.; Courty, N.; Gramfort, A.; Alaya, M. Z.; Boisbunon, A.; Chambon, S.; Chapel, L.; Corenflos, A.; Fatras, K.; Fournier, N.; Gautheron, L.; Gayraud, N. T.; Janati, H.; Rakotomamonjy, A.; Redko, I.; Rolet, A.; Schutz, A.; Seguy, V.; Sutherland, D. J.; Tavenard, R.; Tong, A.; and Vayer, T. 2021 · 2021
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