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Variational inference via Wasserstein gradient flows

Variational inference via Wasserstein gradient flows

31 May 2022
Marc Lambert
Sinho Chewi
Francis R. Bach
Silvère Bonnabel
Philippe Rigollet
    BDL
    DRL
ArXivPDFHTML

Papers citing "Variational inference via Wasserstein gradient flows"

27 / 27 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
Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals
Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals
Alexander Mielke
Jia Jie Zhu
66
1
0
28 Jan 2025
Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity
Shuailong Zhu
Xiaohui Chen
77
0
0
28 Jan 2025
Nested Annealed Training Scheme for Generative Adversarial Networks
Nested Annealed Training Scheme for Generative Adversarial Networks
Chang Wan
Ming-Hsuan Yang
Minglu Li
Yunliang Jiang
Zhonglong Zheng
GAN
38
0
0
20 Jan 2025
Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions
Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions
Shang Wu
Yazhen Wang
43
0
0
11 Jan 2025
Non-geodesically-convex optimization in the Wasserstein space
Non-geodesically-convex optimization in the Wasserstein space
Hoang Phuc Hau Luu
Hanlin Yu
Bernardo Williams
Petrus Mikkola
Marcelo Hartmann
Kai Puolamaki
Arto Klami
57
2
0
08 Jan 2025
Gaussian multi-target filtering with target dynamics driven by a stochastic differential equation
Gaussian multi-target filtering with target dynamics driven by a stochastic differential equation
Á. F. García-Fernández
Simo Särkkä
75
0
0
29 Nov 2024
Wasserstein Flow Matching: Generative modeling over families of distributions
Wasserstein Flow Matching: Generative modeling over families of distributions
Doron Haviv
Aram-Alexandre Pooladian
D. Pe’er
Brandon Amos
OOD
37
0
0
01 Nov 2024
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
C. Margossian
Lawrence K. Saul
31
1
0
14 Oct 2024
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Hoang Phuc Hau Luu
Hanlin Yu
Bernardo Williams
Marcelo Hartmann
Arto Klami
DRL
41
0
0
03 Oct 2024
Particle Semi-Implicit Variational Inference
Particle Semi-Implicit Variational Inference
Jen Ning Lim
A. M. Johansen
51
3
0
30 Jun 2024
Uncertainty Propagation and Bayesian Fusion on Unimodular Lie Groups
  from a Parametric Perspective
Uncertainty Propagation and Bayesian Fusion on Unimodular Lie Groups from a Parametric Perspective
Jikai Ye
Gregory S. Chirikjian
19
1
0
07 Jan 2024
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
Yiheng Jiang
Sinho Chewi
Aram-Alexandre Pooladian
29
7
0
05 Dec 2023
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
Dai Hai Nguyen
Tetsuya Sakurai
Hiroshi Mamitsuka
43
0
0
25 Oct 2023
Accelerating optimization over the space of probability measures
Accelerating optimization over the space of probability measures
Shi Chen
Wenxuan Wu
Yuhang Yao
Stephen J. Wright
29
4
0
06 Oct 2023
VITS : Variational Inference Thompson Sampling for contextual bandits
VITS : Variational Inference Thompson Sampling for contextual bandits
Pierre Clavier
Tom Huix
Alain Durmus
27
3
0
19 Jul 2023
Provable convergence guarantees for black-box variational inference
Provable convergence guarantees for black-box variational inference
Justin Domke
Guillaume Garrigos
Robert Mansel Gower
18
18
0
04 Jun 2023
Forward-backward Gaussian variational inference via JKO in the
  Bures-Wasserstein Space
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space
Michael Diao
Krishnakumar Balasubramanian
Sinho Chewi
Adil Salim
BDL
32
21
0
10 Apr 2023
Particle Mean Field Variational Bayes
Particle Mean Field Variational Bayes
Minh-Ngoc Tran
Paco Tseng
Robert Kohn
32
3
0
24 Mar 2023
Variational Gaussian filtering via Wasserstein gradient flows
Variational Gaussian filtering via Wasserstein gradient flows
Adrien Corenflos
Hany Abdulsamad
20
1
0
11 Mar 2023
An Explicit Expansion of the Kullback-Leibler Divergence along its
  Fisher-Rao Gradient Flow
An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow
Carles Domingo-Enrich
Aram-Alexandre Pooladian
MDE
26
11
0
23 Feb 2023
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient
  Flow
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow
Yuling Yan
Kaizheng Wang
Philippe Rigollet
44
20
0
04 Jan 2023
Regularized Rényi divergence minimization through Bregman proximal
  gradient algorithms
Regularized Rényi divergence minimization through Bregman proximal gradient algorithms
Thomas Guilmeau
Émilie Chouzenoux
Victor Elvira
34
3
0
09 Nov 2022
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
40
22
0
01 Nov 2022
On Representations of Mean-Field Variational Inference
On Representations of Mean-Field Variational Inference
Soumyadip Ghosh
Ying-Ling Lu
T. Nowicki
Edith Zhang
16
1
0
20 Oct 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 unified framework for hard and soft clustering with regularized
  optimal transport
A unified framework for hard and soft clustering with regularized optimal transport
Jean-Frédéric Diebold
Nicolas Papadakis
Arnaud Dessein
Charles-Alban Deledalle
FedML
47
9
0
12 Nov 2017
1