Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1206.6380
Cited By
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
International Conference on Machine Learning (ICML), 2012
27 June 2012
S. Ahn
Anoop Korattikara Balan
Max Welling
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring"
50 / 159 papers shown
Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown
Emile Anand
Sarah Liaw
340
4
0
21 Jul 2025
Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators
Computer Methods in Applied Mechanics and Engineering (CMAME), 2025
Ponkrshnan Thiagarajan
Tamer A. Zaki
Michael D. Shields
BDL
320
1
0
19 Jul 2025
Bayesian Data Sketching for Varying Coefficient Regression Models
Rajarshi Guhaniyogi
Laura Baracaldo
Sudipto Banerjee
170
8
0
30 May 2025
JaxSGMC: Modular stochastic gradient MCMC in JAX
SoftwareX (SoftwareX), 2024
Stephan Thaler
Paul Fuchs
Ana Cukarska
Julija Zavadlav
BDL
562
2
0
16 May 2025
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
International Conference on Learning Representations (ICLR), 2025
Hyunsu Kim
G. Nam
Chulhee Yun
Hongseok Yang
Juho Lee
BDL
UQCV
348
2
0
02 Mar 2025
Variational Bayesian Pseudo-Coreset
International Conference on Learning Representations (ICLR), 2025
Hyungi Lee
Seanie Lee
Juho Lee
BDL
310
0
0
28 Feb 2025
Random Reshuffling for Stochastic Gradient Langevin Dynamics
Luke Shaw
Peter A. Whalley
453
4
0
27 Jan 2025
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
Siyuan Yu
Wei Chen
H. V. Poor
420
0
0
17 Jun 2024
General bounds on the quality of Bayesian coresets
Trevor Campbell
256
3
0
20 May 2024
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Neural Information Processing Systems (NeurIPS), 2024
Claus Hofmann
Simon Schmid
Bernhard Lehner
Daniel Klotz
Sepp Hochreiter
OODD
500
14
0
14 May 2024
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
International Conference on Machine Learning (ICML), 2024
Haoyang Zheng
Hengrong Du
Qi Feng
Wei Deng
Guang Lin
276
8
0
13 May 2024
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning
Omer Subasi
Oceane Bel
Joseph Manzano
Kevin J. Barker
FedML
OOD
PINN
424
4
0
05 Dec 2023
Quantum Langevin Dynamics for Optimization
Communications in Mathematical Physics (CMP), 2023
Zherui Chen
Yuchen Lu
Hao Wang
Yizhou Liu
Tongyang Li
AI4CE
644
23
0
27 Nov 2023
Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions
International Conference on Machine Learning (ICML), 2023
Guneykan Ozgul
Xiantao Li
Mehrdad Mahdavi
Chunhao Wang
267
6
0
17 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates
Federico Danieli
Konstantinos Pitas
M. Vladimirova
Vincent Fortuin
BDL
AAML
420
40
0
28 Sep 2023
Machine Learning and the Future of Bayesian Computation
Steven Winter
Trevor Campbell
Lizhen Lin
Sanvesh Srivastava
David B. Dunson
TPM
442
6
0
21 Apr 2023
Bayesian Pseudo-Coresets via Contrastive Divergence
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Piyush Tiwary
Kumar Shubham
V. Kashyap
Prathosh A.P.
347
4
0
20 Mar 2023
Pigeonhole Stochastic Gradient Langevin Dynamics for Large Crossed Mixed Effects Models
Bayesian Analysis (Bayesian Anal.), 2022
Xinyu Zhang
Cheng Li
264
0
0
18 Dec 2022
Non-reversible Parallel Tempering for Deep Posterior Approximation
AAAI Conference on Artificial Intelligence (AAAI), 2022
Wei Deng
Qian Zhang
Qi Feng
F. Liang
Guang Lin
300
5
0
20 Nov 2022
A Dynamical System View of Langevin-Based Non-Convex Sampling
Neural Information Processing Systems (NeurIPS), 2022
Mohammad Reza Karimi
Ya-Ping Hsieh
Andreas Krause
384
4
0
25 Oct 2022
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
Jeffrey Negrea
Jun Yang
Haoyue Feng
Daniel M. Roy
Jonathan H. Huggins
444
1
0
25 Jul 2022
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
B. Staber
Sébastien Da Veiga
UQCV
BDL
357
3
0
08 Jun 2022
Knowledge Removal in Sampling-based Bayesian Inference
International Conference on Learning Representations (ICLR), 2022
Shaopeng Fu
Fengxiang He
Dacheng Tao
BDL
MU
267
36
0
24 Mar 2022
Bayesian inference via sparse Hamiltonian flows
Neural Information Processing Systems (NeurIPS), 2022
Na Chen
Zuheng Xu
Trevor Campbell
347
14
0
11 Mar 2022
Interacting Contour Stochastic Gradient Langevin Dynamics
International Conference on Learning Representations (ICLR), 2022
Wei Deng
Siqi Liang
Botao Hao
Guang Lin
F. Liang
BDL
288
13
0
20 Feb 2022
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Shiye Lei
Zhuozhuo Tu
Leszek Rutkowski
Feng Zhou
Li Shen
Fengxiang He
Dacheng Tao
BDL
259
2
0
12 Dec 2021
On Convergence of Federated Averaging Langevin Dynamics
Wei Deng
Qian Zhang
Yi-An Ma
Zhao Song
Guang Lin
FedML
483
18
0
09 Dec 2021
Unsupervised PET Reconstruction from a Bayesian Perspective
IEEE International Symposium on Biomedical Imaging (ISBI), 2021
Chenyu Shen
Wenjun Xia
H. Ye
Mingzheng Hou
Hu Chen
Yan Liu
Jiliu Zhou
Yi Zhang
243
3
0
29 Oct 2021
Asynchronous and Distributed Data Augmentation for Massive Data Settings
Jiayuan Zhou
Kshitij Khare
Sanvesh Srivastava
211
4
0
18 Sep 2021
Shift-Curvature, SGD, and Generalization
Arwen V. Bradley
C. Gomez-Uribe
Manish Reddy Vuyyuru
375
3
0
21 Aug 2021
A fast asynchronous MCMC sampler for sparse Bayesian inference
Yves F. Atchadé
Liwei Wang
196
3
0
14 Aug 2021
Batch Normalization Preconditioning for Neural Network Training
Susanna Lange
Kyle E. Helfrich
Qiang Ye
248
17
0
02 Aug 2021
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics
Annales De L Institut Henri Poincare-probabilites Et Statistiques (IHPES), 2021
Alain Durmus
Aurélien Enfroy
Eric Moulines
G. Stoltz
325
18
0
30 Jul 2021
Structured Stochastic Gradient MCMC
International Conference on Machine Learning (ICML), 2021
Antonios Alexos
Alex Boyd
Stephan Mandt
BDL
384
14
0
19 Jul 2021
A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski
Cedrique Rovile Njieutcheu Tassi
Mohsin Ali
Jongseo Lee
Matthias Humt
...
R. Roscher
Muhammad Shahzad
Wen Yang
R. Bamler
Xiaoxiang Zhu
BDL
UQCV
OOD
686
1,678
0
07 Jul 2021
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
International Conference on Machine Learning (ICML), 2021
Giulio Franzese
Dimitrios Milios
Maurizio Filippone
Pietro Michiardi
267
3
0
30 Jun 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2021
Jian Huang
Yuling Jiao
Lican Kang
Xu Liao
Jin Liu
Yanyan Liu
319
26
0
21 Jun 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
Journal of machine learning research (JMLR), 2021
Dong-Young Lim
Sotirios Sabanis
464
13
0
28 May 2021
Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics
Inass Sekkat
G. Stoltz
351
4
0
21 May 2021
Stein's Method Meets Computational Statistics: A Review of Some Recent Developments
Statistical Science (Statist. Sci.), 2021
Andreas Anastasiou
Alessandro Barp
F. Briol
B. Ebner
Robert E. Gaunt
...
Qiang Liu
Lester W. Mackey
Chris J. Oates
Gesine Reinert
Yvik Swan
411
57
0
07 May 2021
A Unifying and Canonical Description of Measure-Preserving Diffusions
Alessandro Barp
So Takao
M. Betancourt
Alexis Arnaudon
Mark Girolami
394
18
0
06 May 2021
What Are Bayesian Neural Network Posteriors Really Like?
International Conference on Machine Learning (ICML), 2021
Pavel Izmailov
Sharad Vikram
Matthew D. Hoffman
A. Wilson
UQCV
BDL
481
450
0
29 Apr 2021
The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms
IFIP International Information Security Conference (IFIP SEC), 2021
Lorena Qendro
Jagmohan Chauhan
Alberto Gil C. P. Ramos
Cecilia Mascolo
225
7
0
11 Feb 2021
Bayesian Inference Forgetting
Shaopeng Fu
Fengxiang He
Yue Xu
Dacheng Tao
MU
360
12
0
16 Jan 2021
Recent advances in deep learning theory
Fengxiang He
Dacheng Tao
AI4CE
402
57
0
20 Dec 2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Neural Information Processing Systems (NeurIPS), 2020
Wei Deng
Guang Lin
F. Liang
BDL
476
35
0
19 Oct 2020
An adaptive Hessian approximated stochastic gradient MCMC method
Journal of Computational Physics (JCP), 2020
Yating Wang
Wei Deng
Guang Lin
BDL
206
5
0
03 Oct 2020
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Sehwan Kim
Qifan Song
F. Liang
BDL
194
14
0
20 Sep 2020
A Survey on Large-scale Machine Learning
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020
Meng Wang
Weijie Fu
Xiangnan He
Shijie Hao
Xindong Wu
222
149
0
10 Aug 2020
Moment-Matching Graph-Networks for Causal Inference
M. Park
CML
BDL
295
0
0
20 Jul 2020
1
2
3
4
Next
Page 1 of 4
