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We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries.
Probable inference, the law of succession, and statistical inference
Edwin B Wilson · 1927
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A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Charles Stein · 1972
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A class of degenerate diffusion processes occurring in population genetics
Stewart N Ethier · 1976
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Problem complexity and method efficiency in optimization
Arkadij Semenovic Nemirovskij and David Borisovich Yudin · 1983
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Stein’s method and Poisson process convergence
Andrew D Barbour · 1988
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Wasserstein control of mirror Langevin Monte Carlo
Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, and Marcelo Pereyra · 2002
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
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Latent Dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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Stochastic Differential Equations: An Introduction with Applications
Bernt Øksendal · 2003
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On learning vector-valued functions
Charles A Micchelli and Massimiliano Pontil · 2005
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Genotypic predictors of human immunodeficiency virus type 1 drug resistance
Soo-Yon Rhee, Jonathan Taylor, Gauhar Wadhera, Asa Ben-Hur, Douglas L Brutlag, and Robert W Shafer · 2006
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Stochastic processes with applications
Rabi N Bhattacharya and Edward C Waymire · 2009
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Eigenvalues of integral operators defined by smooth positive definite kernels
JC Ferreira and VA Menegatto · 2009
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On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization
Sham Kakade, Shai Shalev-Shwartz, Ambuj Tewari, et al · 2009
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Reproducing kernel Hilbert spaces in probability and statistics
Alain Berlinet and Christine Thomas-Agnan · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee W Teh · 2011
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Neural networks for machine learning lecture 6a: overview of mini-batch gradient descent
Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky · 2012
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Stochastic gradient Riemannian Langevin dynamics on the probability simplex
Sam Patterson and Yee Whye Teh · 2013
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Energy statistics: A class of statistics based on distances
Gábor J Székely and Maria L Rizzo · 2013
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The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D Hoffman and Andrew Gelman · 2014
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New insights and perspectives on the natural gradient method
James Martens · 2014
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Langevin diffusions and the Metropolis-adjusted Langevin algorithm
Tatiana Xifara, Chris Sherlock, Samuel Livingstone, Simon Byrne, and Mark Girolami · 2014
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Measuring sample quality with Stein’s method
Jackson Gorham and Lester Mackey · 2015
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A complete recipe for stochastic gradient MCMC
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
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The information geometry of mirror descent
Garvesh Raskutti and Sayan Mukherjee · 2015
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Statistical learning and selective inference
Jonathan Taylor and Robert J Tibshirani · 2015
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A kernel test of goodness of fit
Kacper Chwialkowski, Heiko Strathmann, and Arthur Gretton · 2016
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Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
Alain Durmus, Eric Moulines, and Marcelo Pereyra · 2018
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Mirrored Langevin dynamics
Ya-Ping Hsieh, Ali Kavis, Paul Rolland, and Volkan Cevher · 2018
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Random feature Stein discrepancies
Jonathan Huggins and Lester Mackey · 2018
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Fast yet simple natural-gradient descent for variational inference in complex models
Mohammad Emtiyaz Khan and Didrik Nielsen · 2018
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Bayesian model-agnostic meta-learning
Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Riemannian Stein variational gradient descent for Bayesian inference
Chang Liu and Jun Zhu · 2018
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Reflection couplings and contraction rates for diffusions
Andreas Eberle · 2016
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Exact post-selection inference, with application to the lasso
Jason D Lee, Dennis L Sun, Yuekai Sun, and Jonathan E Taylor · 2016
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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A kernelized Stein discrepancy for goodness-of-fit tests
Qiang Liu, Jason Lee, and Michael Jordan · 2016
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Stochastic quasi-Newton Langevin Monte Carlo
Umut Simsekli, Roland Badeau, Taylan Cemgil, and Gaël Richard · 2016
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MAGIC: a general, powerful and tractable method for selective inference
Xiaoying Tian, Nan Bi, and Jonathan Taylor · 2016
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A spectral approach to gradient estimation for implicit distributions
Jiaxin Shi, Shengyang Sun, and Jun Zhu · 2018
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Stein variational message passing for continuous graphical models
Dilin Wang, Zhe Zeng, and Qiang Liu · 2018
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Message passing Stein variational gradient descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang · 2018
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Minimum Stein discrepancy estimators
Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, and Lester Mackey · 2019
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Projected Stein variational Newton: A fast and scalable Bayesian inference method in high dimensions
Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, and Omar Ghattas · 2019
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On the geometry of Stein variational gradient descent
Andrew Duncan, Nikolas Nüsken, and Lukasz Szpruch · 2019
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Bayesian posterior approximation via greedy particle optimization
Futoshi Futami, Zhenghang Cui, Issei Sato, and Masashi Sugiyama · 2019
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Measuring sample quality with diffusions
Jackson Gorham, Andrew B Duncan, Sebastian J Vollmer, and Lester Mackey · 2019
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Is there an analog of Nesterov acceleration for MCMC?
Yi-An Ma, Niladri Chatterji, Xiang Cheng, Nicolas Flammarion, Peter Bartlett, and Michael I Jordan · 2019
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selectiveInference: Tools for Post-Selection Inference , 2019
Ryan Tibshirani, Rob Tibshirani, Jonatha Taylor, Joshua Loftus, Stephen Reid, and Jelena Markovic · 2019
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Stein variational gradient descent with matrix-valued kernels
Dilin Wang, Ziyang Tang, Chandrajit Bajaj, and Qiang Liu · 2019
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Efficient constrained sampling via the mirror-Langevin algorithm
Kwangjun Ahn and Sinho Chewi · 2020
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Exponential ergodicity of mirror-Langevin diffusions
Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, and Austin Stromme · 2020
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Stochastic stein discrepancies
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A non-asymptotic analysis for Stein variational gradient descent
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, and Arthur Gretton · 2020
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Optimal thinning of MCMC output
Marina Riabiz, Wilson Chen, Jon Cockayne, Pawel Swietach, Steven A Niederer, Lester Mackey, Chris Oates, et al · 2020
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Variance reduction and quasi-Newton for particle-based variational inference
Michael Zhu, Chang Liu, and Jun Zhu · 2020
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