Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2002.03469
Cited By
v1
v2 (latest)
Projected Stein Variational Gradient Descent
Neural Information Processing Systems (NeurIPS), 2020
9 February 2020
Peng Chen
Omar Ghattas
BDL
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Projected Stein Variational Gradient Descent"
36 / 36 papers shown
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Yunuo Zhang
Baiting Luo
Ayan Mukhopadhyay
G. Karsai
Abhishek Dubey
97
0
0
24 Oct 2025
Are We Really Learning the Score Function? Reinterpreting Diffusion Models Through Wasserstein Gradient Flow Matching
An B. Vuong
Michael T. McCann
Javier E. Santos
Yen Ting Lin
DiffM
104
2
0
30 Aug 2025
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
Xinjian Zhao
Yutian Xiao
Binhao Wang
Sheng Zhang
Shanshan Ye
Wanyu Wang
Hongzhi Yin
Ruocheng Guo
Zenglin Xu
232
5
0
07 Jul 2025
Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification
Griffin Tabor
Tucker Hermans
165
2
0
31 May 2025
Path-Guided Particle-based Sampling
International Conference on Machine Learning (ICML), 2024
Mingzhou Fan
Ruida Zhou
C. Tian
Xiaoning Qian
299
9
0
04 Dec 2024
Stein Variational Newton Neural Network Ensembles
Klemens Flöge
Mohammed Abdul Moeed
Vincent Fortuin
BDL
UQCV
324
0
0
04 Nov 2024
Dimension reduction via score ratio matching
Ricardo Baptista
Michael C. Brennan
Youssef Marzouk
289
1
0
25 Oct 2024
A Trust-Region Method for Graphical Stein Variational Inference
Conference on Uncertainty in Artificial Intelligence (UAI), 2024
Liam Pavlovic
David M. Rosen
223
0
0
21 Oct 2024
Annealed Stein Variational Gradient Descent for Improved Uncertainty Estimation in Full-Waveform Inversion
Geophysical Journal International (GJI), 2024
M. Corrales
Sean Berti
Bertrand Denel
Paul Williamson
Mattia Aleardi
M. Ravasi
188
4
0
17 Oct 2024
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
G. A. Padmanabha
J. Fuhg
Cosmin Safta
Reese E. Jones
N. Bouklas
297
10
0
30 Jun 2024
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
J. Hauth
Cosmin Safta
Xun Huan
Ravi G. Patel
Reese E. Jones
BDL
UQCV
335
2
0
17 Feb 2024
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
Haeju Lee
Minchan Jeong
SeYoung Yun
Kee-Eung Kim
AAML
VPVLM
235
4
0
13 Feb 2024
Convergence and stability results for the particle system in the Stein gradient descent method
José A. Carrillo
Jakub Skrzeczkowski
202
6
0
26 Dec 2023
Learning Rate Free Sampling in Constrained Domains
Louis Sharrock
Lester W. Mackey
Christopher Nemeth
396
4
0
24 May 2023
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
Neural Information Processing Systems (NeurIPS), 2023
Tianle Liu
Promit Ghosal
Krishnakumar Balasubramanian
Natesh S. Pillai
521
16
0
23 May 2023
Augmented Message Passing Stein Variational Gradient Descent
Jiankui Zhou
Yue Qiu
194
0
0
18 May 2023
Score Operator Newton transport
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
N. Chandramoorthy
F. Schaefer
Youssef Marzouk
OT
383
1
0
16 May 2023
Principal Feature Detection via
Φ
Φ
Φ
-Sobolev Inequalities
Bernoulli (Bernoulli), 2023
Matthew T.C. Li
Youssef Marzouk
O. Zahm
250
14
0
10 May 2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
Mechanical systems and signal processing (MSSP), 2023
V. Nemani
Luca Biggio
Xun Huan
Zhen Hu
Olga Fink
Anh Tran
Yan Wang
Xiaoge Zhang
Chao Hu
AI4CE
297
141
0
07 May 2023
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Sebastian Reich
Andrew M. Stuart
822
23
0
21 Feb 2023
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
International Conference on Machine Learning (ICML), 2023
Louis Sharrock
Christopher Nemeth
BDL
422
10
0
26 Jan 2023
Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective
Ricardo Baptista
Youssef Marzouk
O. Zahm
235
17
0
18 Jul 2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Journal of Computational Physics (JCP), 2022
Thomas O'Leary-Roseberry
Peng Chen
Umberto Villa
Omar Ghattas
AI4CE
351
59
0
21 Jun 2022
Feature Space Particle Inference for Neural Network Ensembles
International Conference on Machine Learning (ICML), 2022
Shingo Yashima
Teppei Suzuki
Kohta Ishikawa
Ikuro Sato
Rei Kawakami
BDL
340
12
0
02 Jun 2022
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization
Computer Methods in Applied Mechanics and Engineering (CMAME), 2022
Vahid Keshavarzzadeh
Robert M. Kirby
A. Narayan
BDL
210
3
0
07 May 2022
A stochastic Stein Variational Newton method
Alex Leviyev
Joshua Chen
Yifei Wang
Omar Ghattas
A. Zimmerman
172
10
0
19 Apr 2022
Grassmann Stein Variational Gradient Descent
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Xingtu Liu
Harrison Zhu
Jean-François Ton
George Wynne
Andrew Duncan
305
15
0
07 Feb 2022
Neural Variational Gradient Descent
L. Langosco
Vincent Fortuin
Heiko Strathmann
BDL
406
23
0
22 Jul 2021
Stein Variational Gradient Descent with Multiple Kernel
Cognitive Computation (Cogn Comput), 2021
Qingzhong Ai
Shiyu Liu
Lirong He
Zenglin Xu
353
8
0
20 Jul 2021
Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation
Louis Sharrock
N. Kantas
P. Parpas
G. Pavliotis
245
31
0
25 Jun 2021
On Stein Variational Neural Network Ensembles
Francesco DÁngelo
Vincent Fortuin
F. Wenzel
UQCV
BDL
307
31
0
20 Jun 2021
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
International Conference on Machine Learning (ICML), 2021
Shumao Zhang
Pengchuan Zhang
Daniel Leibovici
BDL
209
6
0
12 May 2021
Projected Wasserstein gradient descent for high-dimensional Bayesian inference
Yifei Wang
Peng Chen
Wuchen Li
237
32
0
12 Feb 2021
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
Computer Methods in Applied Mechanics and Engineering (CMAME), 2020
Thomas O'Leary-Roseberry
Umberto Villa
Peng Chen
Omar Ghattas
343
79
0
30 Nov 2020
Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
Peng Chen
Omar Ghattas
248
22
0
19 Nov 2020
Sliced Kernelized Stein Discrepancy
Wenbo Gong
Yingzhen Li
José Miguel Hernández-Lobato
357
41
0
30 Jun 2020
1
Page 1 of 1
