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Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural
  Networks

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks

23 December 2015
Chunyuan Li
Changyou Chen
David Carlson
Lawrence Carin
    ODLBDL
ArXiv (abs)PDFHTML

Papers citing "Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks"

50 / 189 papers shown
Title
Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
Rajit Rajpal
Benedict Leimkuhler
Yuanhao Jiang
BDL
399
0
0
11 Nov 2025
Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory
Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory
Einar Urdshals
Edmund Lau
Jesse Hoogland
Stan van Wingerden
Daniel Murfet
103
1
0
14 Oct 2025
Influence Dynamics and Stagewise Data Attribution
Influence Dynamics and Stagewise Data Attribution
Jin Hwa Lee
Matthew Smith
Maxwell Adam
Jesse Hoogland
TDIAI4TS
177
0
0
14 Oct 2025
Flatness-Aware Stochastic Gradient Langevin Dynamics
Flatness-Aware Stochastic Gradient Langevin Dynamics
Stefano Bruno
Youngsik Hwang
Jaehyeon An
Sotirios Sabanis
Dong-Young Lim
152
0
0
02 Oct 2025
Bayesian Influence Functions for Hessian-Free Data Attribution
Bayesian Influence Functions for Hessian-Free Data Attribution
Philipp Alexander Kreer
Wilson Wu
Maxwell Adam
Zach Furman
Jesse Hoogland
TDIBDL
203
2
0
30 Sep 2025
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural ProcessInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Lingkai Kong
Haotian Sun
Yuchen Zhuang
Haorui Wang
Wenhao Mu
Chao Zhang
BDL
148
5
0
23 Aug 2025
From Global to Local: A Scalable Benchmark for Local Posterior Sampling
From Global to Local: A Scalable Benchmark for Local Posterior Sampling
Rohan Hitchcock
Jesse Hoogland
138
1
0
29 Jul 2025
Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown
Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown
Emile Anand
Sarah Liaw
212
0
0
21 Jul 2025
Conformal Object Detection by Sequential Risk Control
Conformal Object Detection by Sequential Risk Control
Léo Andéol
Luca Mossina
Adrien Mazoyer
Sébastien Gerchinovitz
324
0
0
29 May 2025
Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision Layer
Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision LayerInternational Conference on Learning Representations (ICLR), 2025
Xinyue Hu
Zhibin Duan
Bo Chen
Mingyuan Zhou
UQCVBDL
332
2
0
28 May 2025
Training Latent Diffusion Models with Interacting Particle Algorithms
Training Latent Diffusion Models with Interacting Particle Algorithms
Tim Y. J. Wang
Juan Kuntz
O. Deniz Akyildiz
413
3
0
18 May 2025
JaxSGMC: Modular stochastic gradient MCMC in JAX
JaxSGMC: Modular stochastic gradient MCMC in JAXSoftwareX (SoftwareX), 2024
Stephan Thaler
Paul Fuchs
Ana Cukarska
Julija Zavadlav
BDL
408
2
0
16 May 2025
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties
Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties
Jiaxiang Yi
Miguel A. Bessa
UDPERUQCV
317
2
0
05 May 2025
Parameter Expanded Stochastic Gradient Markov Chain Monte CarloInternational Conference on Learning Representations (ICLR), 2025
Hyunsu Kim
G. Nam
Chulhee Yun
Hongseok Yang
Juho Lee
BDLUQCV
237
0
0
02 Mar 2025
Bayesian Computation in Deep Learning
Bayesian Computation in Deep Learning
Wenlong Chen
Bolian Li
Ruqi Zhang
Yingzhen Li
BDL
476
1
0
25 Feb 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
Haoyang Zheng
Guang Lin
AI4CE
192
1
0
01 Feb 2025
Stochastic Process Learning via Operator Flow Matching
Stochastic Process Learning via Operator Flow Matching
Yaozhong Shi
Zachary E. Ross
D. Asimaki
Kamyar Azizzadenesheli
506
5
0
07 Jan 2025
BayesNAM: Leveraging Inconsistency for Reliable Explanations
BayesNAM: Leveraging Inconsistency for Reliable Explanations
Hoki Kim
Jinseong Park
Yujin Choi
Seungyun Lee
Jaewook Lee
BDL
145
1
0
10 Nov 2024
Gradient Methods with Online Scaling
Gradient Methods with Online ScalingAnnual Conference Computational Learning Theory (COLT), 2024
Wenzhi Gao
Ya-Chi Chu
Yinyu Ye
Madeleine Udell
287
13
0
04 Nov 2024
Functional Stochastic Gradient MCMC for Bayesian Neural Networks
Functional Stochastic Gradient MCMC for Bayesian Neural NetworksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Mengjing Wu
Junyu Xuan
Jie Lu
BDL
271
1
0
25 Sep 2024
Learning to Explore for Stochastic Gradient MCMC
Learning to Explore for Stochastic Gradient MCMCInternational Conference on Machine Learning (ICML), 2024
Seunghyun Kim
Seohyeon Jung
Seonghyeon Kim
Juho Lee
BDL
270
1
0
17 Aug 2024
Particle Semi-Implicit Variational Inference
Particle Semi-Implicit Variational Inference
Jen Ning Lim
A. M. Johansen
359
11
0
30 Jun 2024
Reparameterization invariance in approximate Bayesian inference
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy
M. Miani
Carl Henrik Ek
Philipp Hennig
Marvin Pfortner
Lukas Tatzel
Søren Hauberg
BDL
325
14
0
05 Jun 2024
Scalable Bayesian Learning with posteriors
Scalable Bayesian Learning with posteriors
Samuel Duffield
Kaelan Donatella
Johnathan Chiu
Phoebe Klett
Daniel Simpson
BDLUQCV
496
4
0
31 May 2024
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear
  Inverse Problems
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems
Lorenzo Baldassari
Ali Siahkoohi
Josselin Garnier
K. Sølna
Maarten V. de Hoop
DiffM
300
3
0
24 May 2024
Stochastic Gradient MCMC for Massive Geostatistical Data
Stochastic Gradient MCMC for Massive Geostatistical Data
M. Abba
Brian J. Reich
Reetam Majumder
Brandon Feng
177
1
0
07 May 2024
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for
  Undersampled MRI Reconstruction with Uncertainty Estimation
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimation
Samah Khawaled
Moti Freiman
UQCV
158
5
0
06 Apr 2024
Fast Value Tracking for Deep Reinforcement Learning
Fast Value Tracking for Deep Reinforcement Learning
Frank Shih
Faming Liang
BDL
113
4
0
19 Mar 2024
On the Convergence of Locally Adaptive and Scalable Diffusion-Based
  Sampling Methods for Deep Bayesian Neural Network Posteriors
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network PosteriorsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Tim Rensmeyer
Oliver Niggemann
UQCVBDLOODMedIm
155
1
0
13 Mar 2024
Improving Implicit Regularization of SGD with Preconditioning for Least
  Square Problems
Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems
Junwei Su
Difan Zou
Chuan Wu
384
0
0
13 Mar 2024
Scalable Bayesian inference for the generalized linear mixed model
Scalable Bayesian inference for the generalized linear mixed model
S. Berchuck
Felipe A. Medeiros
Sayan Mukherjee
Andrea Agazzi
182
0
0
05 Mar 2024
Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
Sikun Yang
Heinz Koeppl
193
0
0
29 Feb 2024
Weight fluctuations in (deep) linear neural networks and a derivation of
  the inverse-variance flatness relation
Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relationPhysical Review Research (Phys. Rev. Res.), 2023
Markus Gross
A. Raulf
Christoph Räth
420
0
0
23 Nov 2023
Bayesian Domain Invariant Learning via Posterior Generalization of
  Parameter Distributions
Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions
Shiyu Shen
Bin Pan
Tianyang Shi
Tao Li
Zhenwei Shi
BDLOOD
260
1
0
25 Oct 2023
Be Bayesian by Attachments to Catch More Uncertainty
Be Bayesian by Attachments to Catch More Uncertainty
Shiyu Shen
Bin Pan
Tianyang Shi
Tao Li
Zhenwei Shi
UQCV
290
0
0
19 Oct 2023
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
S. Bieringer
Gregor Kasieczka
Maximilian F. Steffen
Mathias Trabs
294
0
0
13 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates
A Primer on Bayesian Neural Networks: Review and Debates
Federico Danieli
Konstantinos Pitas
M. Vladimirova
Vincent Fortuin
BDLAAML
243
34
0
28 Sep 2023
A Probabilistic Approach to Self-Supervised Learning using Cyclical
  Stochastic Gradient MCMC
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC
Masoumeh Javanbakhat
Christoph Lippert
SSLBDL
100
1
0
02 Aug 2023
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
BayesDAG: Gradient-Based Posterior Inference for Causal DiscoveryNeural Information Processing Systems (NeurIPS), 2023
Yashas Annadani
Nick Pawlowski
Joel Jennings
Stefan Bauer
Cheng Zhang
Wenbo Gong
CMLBDL
248
30
0
26 Jul 2023
High-Rate Phase Association with Travel Time Neural Fields
High-Rate Phase Association with Travel Time Neural Fields
Chengzhi Shi
Maarten V. de Hoop
Ivan Dokmanić
235
1
0
14 Jul 2023
Quantification of Uncertainty with Adversarial Models
Quantification of Uncertainty with Adversarial ModelsNeural Information Processing Systems (NeurIPS), 2023
Kajetan Schweighofer
L. Aichberger
Mykyta Ielanskyi
Günter Klambauer
Sepp Hochreiter
UQCV
262
28
0
06 Jul 2023
MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuning
MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuningInternational Joint Conference on Artificial Intelligence (IJCAI), 2023
Zhehua Zhong
Tianyi Chen
Zhen Wang
AAML
119
4
0
27 Jun 2023
A New Paradigm for Generative Adversarial Networks based on Randomized
  Decision Rules
A New Paradigm for Generative Adversarial Networks based on Randomized Decision RulesStatistica sinica (Stat. Sinica), 2023
Sehwan Kim
Qifan Song
Faming Liang
GAN
94
1
0
23 Jun 2023
Fit Like You Sample: Sample-Efficient Generalized Score Matching from
  Fast Mixing Diffusions
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing DiffusionsAnnual Conference Computational Learning Theory (COLT), 2023
Yilong Qin
Andrej Risteski
DiffM
313
2
0
15 Jun 2023
Gibbs Sampling the Posterior of Neural Networks
Gibbs Sampling the Posterior of Neural Networks
Giovanni Piccioli
Emanuele Troiani
Lenka Zdeborová
205
3
0
05 Jun 2023
A General Framework for Uncertainty Quantification via Neural SDE-RNN
A General Framework for Uncertainty Quantification via Neural SDE-RNN
Shweta Dahale
Sai Munikoti
Balasubramaniam Natarajan
AI4TSUQCVBDL
128
3
0
01 Jun 2023
Provable and Practical: Efficient Exploration in Reinforcement Learning
  via Langevin Monte Carlo
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloInternational Conference on Learning Representations (ICLR), 2023
Haque Ishfaq
Qingfeng Lan
Pan Xu
A. R. Mahmood
Doina Precup
Anima Anandkumar
Kamyar Azizzadenesheli
BDLOffRL
312
27
0
29 May 2023
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo
  Algorithms
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
Tim Tsz-Kit Lau
Han Liu
Thomas Pock
366
7
0
25 May 2023
Tuning-Free Maximum Likelihood Training of Latent Variable Models via
  Coin Betting
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin BettingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Louis Sharrock
Daniel Dodd
Christopher Nemeth
214
10
0
24 May 2023
Particle Mean Field Variational Bayes
Particle Mean Field Variational Bayes
Minh-Ngoc Tran
Paco Tseng
Robert Kohn
249
4
0
24 Mar 2023
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