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Kernel Adaptive Metropolis-Hastings
v1v2v3 (latest)

Kernel Adaptive Metropolis-Hastings

19 July 2013
Dino Sejdinovic
Heiko Strathmann
M. Garcia
Christophe Andrieu
Arthur Gretton
ArXiv (abs)PDFHTMLGithub (33★)

Papers citing "Kernel Adaptive Metropolis-Hastings"

29 / 29 papers shown
Title
Stochastic mirror descent for nonparametric adaptive importance sampling
Stochastic mirror descent for nonparametric adaptive importance sampling
Pascal Bianchi
B. Delyon
Victor Priser
François Portier
58
3
0
20 Sep 2024
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Alexander Kolesov
Petr Mokrov
Igor Udovichenko
Milena Gazdieva
G. Pammer
Anastasis Kratsios
Evgeny Burnaev
Alexander Korotin
OT
128
3
0
02 Oct 2023
Cyclical Kernel Adaptive Metropolis
Cyclical Kernel Adaptive Metropolis
J. Li
Yimeng Zeng
Wen-Ping Guo
18
0
0
29 Jun 2022
RKHS-SHAP: Shapley Values for Kernel Methods
RKHS-SHAP: Shapley Values for Kernel Methods
Siu Lun Chau
Robert Hu
Javier I. González
Dino Sejdinovic
FAtt
83
20
0
18 Oct 2021
Semi-Empirical Objective Functions for MCMC Proposal Optimization
Semi-Empirical Objective Functions for MCMC Proposal Optimization
Chris Cannella
Vahid Tarokh
65
1
0
03 Jun 2021
Active Learning for Deep Gaussian Process Surrogates
Active Learning for Deep Gaussian Process Surrogates
Annie Sauer
R. Gramacy
D. Higdon
GPAI4CE
71
92
0
15 Dec 2020
Adaptive Path Sampling in Metastable Posterior Distributions
Adaptive Path Sampling in Metastable Posterior Distributions
Yuling Yao
Collin Cademartori
Aki Vehtari
Andrew Gelman
TPM
61
6
0
01 Sep 2020
Scalable Approximate Inference and Some Applications
Scalable Approximate Inference and Some Applications
Jun Han
BDL
52
1
0
07 Mar 2020
Hug and Hop: a discrete-time, non-reversible Markov chain Monte-Carlo
  algorithm
Hug and Hop: a discrete-time, non-reversible Markov chain Monte-Carlo algorithm
Matthew Ludkin
Chris Sherlock
42
8
0
29 Jul 2019
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Simone Rossi
Sébastien Marmin
Maurizio Filippone
BDL
99
16
0
27 May 2019
Adaptive MCMC via Combining Local Samplers
Adaptive MCMC via Combining Local Samplers
Kiarash Shaloudegi
András Gyorgy
64
1
0
11 Jun 2018
A Spectral Approach to Gradient Estimation for Implicit Distributions
A Spectral Approach to Gradient Estimation for Implicit Distributions
Jiaxin Shi
Shengyang Sun
Jun Zhu
83
92
0
07 Jun 2018
Stein Variational Gradient Descent Without Gradient
Stein Variational Gradient Descent Without Gradient
J. Han
Qiang Liu
92
45
0
07 Jun 2018
Air Markov Chain Monte Carlo
Air Markov Chain Monte Carlo
C. Chimisov
Krzysztof Latuszynski
Gareth O. Roberts
82
11
0
28 Jan 2018
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert
  Spaces
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Stefan Klus
Ingmar Schuster
Krikamol Muandet
91
122
0
05 Dec 2017
Generalizing Hamiltonian Monte Carlo with Neural Networks
Generalizing Hamiltonian Monte Carlo with Neural Networks
Daniel Levy
Matthew D. Hoffman
Jascha Narain Sohl-Dickstein
BDL
85
130
0
25 Nov 2017
Pseudo-extended Markov chain Monte Carlo
Pseudo-extended Markov chain Monte Carlo
Christopher Nemeth
Fredrik Lindsten
Maurizio Filippone
J. Hensman
74
10
0
17 Aug 2017
Efficient and principled score estimation with Nyström kernel
  exponential families
Efficient and principled score estimation with Nyström kernel exponential families
Danica J. Sutherland
Heiko Strathmann
Michael Arbel
Arthur Gretton
84
24
0
23 May 2017
Active and Transfer Learning of Grasps by Kernel Adaptive MCMC
Active and Transfer Learning of Grasps by Kernel Adaptive MCMC
P. Zech
Hanchen Xiong
J. Piater
17
0
0
19 Nov 2016
Active and Transfer Learning of Grasps by Sampling from Demonstration
Active and Transfer Learning of Grasps by Sampling from Demonstration
P. Zech
J. Piater
28
2
0
19 Nov 2016
Grasp Learning by Sampling from Demonstration
Grasp Learning by Sampling from Demonstration
P. Zech
J. Piater
37
3
0
19 Nov 2016
Uncertain programming model for multi-item solid transportation problem
Uncertain programming model for multi-item solid transportation problem
Hasan Dalman
140
64
0
31 May 2016
Kernel Sequential Monte Carlo
Kernel Sequential Monte Carlo
Ingmar Schuster
Heiko Strathmann
Brooks Paige
Dino Sejdinovic
BDL
45
7
0
11 Oct 2015
Gradient Importance Sampling
Gradient Importance Sampling
Ingmar Schuster
89
25
0
21 Jul 2015
Geometric ergodicity of the Random Walk Metropolis with
  position-dependent proposal covariance
Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance
Samuel Livingstone
167
11
0
21 Jul 2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential
  Families
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Heiko Strathmann
Dino Sejdinovic
Samuel Livingstone
Z. Szabó
Arthur Gretton
BDL
100
76
0
08 Jun 2015
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Mijung Park
Wittawat Jitkrittum
Dino Sejdinovic
74
10
0
09 Feb 2015
A Wild Bootstrap for Degenerate Kernel Tests
A Wild Bootstrap for Degenerate Kernel Tests
Kacper P. Chwialkowski
Dino Sejdinovic
Arthur Gretton
86
56
0
23 Aug 2014
Information-geometric Markov Chain Monte Carlo methods using Diffusions
Information-geometric Markov Chain Monte Carlo methods using Diffusions
Samuel Livingstone
Mark Girolami
DiffM
116
45
0
31 Mar 2014
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