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Sampling via Gradient Flows in the Space of Probability Measures

Sampling via Gradient Flows in the Space of Probability Measures

5 October 2023
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Sebastian Reich
Andrew M. Stuart
ArXivPDFHTML

Papers citing "Sampling via Gradient Flows in the Space of Probability Measures"

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 A. Atanasov
36
0
0
06 May 2025
New affine invariant ensemble samplers and their dimensional scaling
New affine invariant ensemble samplers and their dimensional scaling
Yifan Chen
16
0
0
05 May 2025
Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and
  Cauchy Problems on Quantile Functions
Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantile Functions
Richard Duong
Viktor Stein
Robert Beinert
J. Hertrich
Gabriele Steidl
33
2
0
14 Aug 2024
Importance Corrected Neural JKO Sampling
Importance Corrected Neural JKO Sampling
Johannes Hertrich
Robert Gruhlke
30
1
0
29 Jul 2024
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
José A. Carrillo
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Dongyi Wei
AI4CE
24
3
0
22 Jul 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
43
5
0
25 Jun 2024
Sequential-in-time training of nonlinear parametrizations for solving
  time-dependent partial differential equations
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
Huan Zhang
Yifan Chen
Eric Vanden-Eijnden
Benjamin Peherstorfer
36
2
0
01 Apr 2024
Ensemble-Based Annealed Importance Sampling
Ensemble-Based Annealed Importance Sampling
Haoxuan Chen
Lexing Ying
33
2
0
28 Jan 2024
Affine Invariant Ensemble Transform Methods to Improve Predictive
  Uncertainty in Neural Networks
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks
Diksha Bhandari
Jakiw Pidstrigach
Sebastian Reich
25
1
0
09 Sep 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
32
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
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for
  Log-Concave Sampling
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling
Keru Wu
S. Schmidler
Yuansi Chen
39
50
0
27 Sep 2021
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
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
185
3,262
0
09 Jun 2012
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