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Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations
  and Affine Invariance

Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance

21 February 2023
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Sebastian Reich
Andrew M. Stuart
ArXivPDFHTML

Papers citing "Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance"

14 / 14 papers shown
Title
Variational Formulation of the Particle Flow Particle Filter
Variational Formulation of the Particle Flow Particle Filter
Yinzhuang Yi
Jorge Cortés
Nikolay Atanasov
36
0
0
06 May 2025
Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flow
Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flow
Matthias Liero
Alexander Mielke
Oliver Tse
Jia Jie Zhu
31
0
0
29 Apr 2025
Kernel Approximation of Fisher-Rao Gradient Flows
Kernel Approximation of Fisher-Rao Gradient Flows
Jia Jie Zhu
Alexander Mielke
46
5
0
27 Oct 2024
Efficient, Multimodal, and Derivative-Free Bayesian Inference With
  Fisher-Rao Gradient Flows
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Sebastian Reich
Andrew M. Stuart
51
5
0
25 Jun 2024
Doubly Adaptive Importance Sampling
Doubly Adaptive Importance Sampling
W. van den Boom
Andrea Cremaschi
Alexandre H. Thiery
26
0
0
29 Apr 2024
Measure transport with kernel mean embeddings
Measure transport with kernel mean embeddings
Linfeng Wang
Nikolas Nusken
34
7
0
23 Jan 2024
Sampling in Unit Time with Kernel Fisher-Rao Flow
Sampling in Unit Time with Kernel Fisher-Rao Flow
A. Maurais
Youssef Marzouk
26
12
0
08 Jan 2024
Sampling via Gradient Flows in the Space of Probability Measures
Sampling via Gradient Flows in the Space of Probability Measures
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Sebastian Reich
Andrew M. Stuart
30
13
0
05 Oct 2023
Towards Understanding the Dynamics of Gaussian-Stein Variational
  Gradient Descent
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
Tianle Liu
Promit Ghosal
Krishnakumar Balasubramanian
Natesh S. Pillai
27
9
0
23 May 2023
Birth-death dynamics for sampling: Global convergence, approximations
  and their asymptotics
Birth-death dynamics for sampling: Global convergence, approximations and their asymptotics
Yulong Lu
D. Slepčev
Lihan Wang
34
22
0
01 Nov 2022
Optimal Neural Network Approximation of Wasserstein Gradient Direction
  via Convex Optimization
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization
Yifei Wang
Peng Chen
Mert Pilanci
Wuchen Li
35
8
0
26 May 2022
Projected Stein Variational Gradient Descent
Projected Stein Variational Gradient Descent
Peng Chen
Omar Ghattas
BDL
55
68
0
09 Feb 2020
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan
Didrik Nielsen
Voot Tangkaratt
Wu Lin
Y. Gal
Akash Srivastava
ODL
74
266
0
13 Jun 2018
A Stein variational Newton method
A Stein variational Newton method
Gianluca Detommaso
Tiangang Cui
Alessio Spantini
Youssef Marzouk
Robert Scheichl
61
114
0
08 Jun 2018
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