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Convergence Rates of Variational Inference in Sparse Deep Learning
v1v2 (latest)

Convergence Rates of Variational Inference in Sparse Deep Learning

International Conference on Machine Learning (ICML), 2019
9 August 2019
Badr-Eddine Chérief-Abdellatif
    BDL
ArXiv (abs)PDFHTML

Papers citing "Convergence Rates of Variational Inference in Sparse Deep Learning"

31 / 31 papers shown
SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions
SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions
Ziyi Wang
Nan Jiang
Guang Lin
Qifan Song
MQ
252
0
0
10 Oct 2025
Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality
Kyeongwon Lee
Lizhen Lin
Jaewoo Park
Seonghyun Jeong
178
0
0
23 Jun 2025
Variational Bayesian Bow tie Neural Networks with Shrinkage
Alisa Sheinkman
Sara Wade
BDLUQCV
417
0
0
17 Nov 2024
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier
William Kengne
391
4
0
29 Oct 2024
Posterior and variational inference for deep neural networks with heavy-tailed weights
Posterior and variational inference for deep neural networks with heavy-tailed weights
Ismael Castillo
Paul Egels
BDL
365
10
0
05 Jun 2024
Misclassification bounds for PAC-Bayesian sparse deep learning
Misclassification bounds for PAC-Bayesian sparse deep learningMachine-mediated learning (ML), 2024
The Tien Mai
UQCVBDL
369
8
0
02 May 2024
Deep Horseshoe Gaussian Processes
Deep Horseshoe Gaussian Processes
Ismael Castillo
Thibault Randrianarisoa
BDLUQCV
379
7
0
04 Mar 2024
Improved Convergence Rate of Nested Simulation with LSE on Sieve
Improved Convergence Rate of Nested Simulation with LSE on SieveJournal of the Operations Research Society of China (JORSC), 2023
Ruoxue Liu
Liang Ding
Wei Cao
Lu Zou
194
0
0
18 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
344
1
0
13 Oct 2023
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated
  Learning with Bayesian Inference-Based Adaptive Dropout
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive DropoutIEEE International Parallel and Distributed Processing Symposium (IPDPS), 2023
Jingjing Xue
Min Liu
Sheng Sun
Yuwei Wang
Hui Jiang
Xue Jiang
343
8
0
14 Jul 2023
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Shiyu Liu
Shaogao Lv
Dun Zeng
Zenglin Xu
Hongya Wang
Yue Yu
FedML
223
5
0
05 Jul 2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its
  Posterior Inference
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceInternational Conference on Machine Learning (ICML), 2023
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Gyuseung Baek
Yongdai Kim
BDL
266
8
0
24 May 2023
FedHB: Hierarchical Bayesian Federated Learning
FedHB: Hierarchical Bayesian Federated Learning
Minyoung Kim
Timothy M. Hospedales
FedML
231
9
0
08 May 2023
Variational Inference for Bayesian Neural Networks under Model and
  Parameter Uncertainty
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
A. Hubin
G. Storvik
BDLUQCV
390
6
0
01 May 2023
Federated Learning via Variational Bayesian Inference: Personalization,
  Sparsity and Clustering
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
Xu Zhang
Wenpeng Li
Yunfeng Shao
Yinchuan Li
FedML
212
6
0
08 Mar 2023
Statistical and Computational Trade-offs in Variational Inference: A
  Case Study in Inferential Model Selection
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
Kush S. Bhatia
Nikki Lijing Kuang
Yi-An Ma
Yixin Wang
230
8
0
22 Jul 2022
Personalized Federated Learning via Variational Bayesian Inference
Personalized Federated Learning via Variational Bayesian InferenceInternational Conference on Machine Learning (ICML), 2022
Xu Zhang
Yinchuan Li
Wenpeng Li
Kaiyang Guo
Yunfeng Shao
FedML
301
127
0
16 Jun 2022
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Yongdai Kim
TPM
331
1
0
02 Jun 2022
Asymptotic Properties for Bayesian Neural Network in Besov Space
Asymptotic Properties for Bayesian Neural Network in Besov SpaceNeural Information Processing Systems (NeurIPS), 2022
Kyeongwon Lee
Jaeyong Lee
BDL
262
5
0
01 Jun 2022
On the inability of Gaussian process regression to optimally learn
  compositional functions
On the inability of Gaussian process regression to optimally learn compositional functionsNeural Information Processing Systems (NeurIPS), 2022
M. Giordano
Kolyan Ray
Johannes Schmidt-Hieber
373
17
0
16 May 2022
A PAC-Bayes oracle inequality for sparse neural networks
A PAC-Bayes oracle inequality for sparse neural networks
Maximilian F. Steffen
Mathias Trabs
UQCV
228
3
0
26 Apr 2022
User-friendly introduction to PAC-Bayes bounds
User-friendly introduction to PAC-Bayes bounds
Pierre Alquier
FedML
685
270
0
21 Oct 2021
Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical
  Guarantees and Implementation Details
Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical Guarantees and Implementation DetailsNeural Networks (NN), 2021
Sanket Jantre
Shrijita Bhattacharya
T. Maiti
BDL
311
19
0
25 Aug 2021
A fast asynchronous MCMC sampler for sparse Bayesian inference
A fast asynchronous MCMC sampler for sparse Bayesian inference
Yves F. Atchadé
Liwei Wang
165
3
0
14 Aug 2021
Efficient Variational Inference for Sparse Deep Learning with
  Theoretical Guarantee
Efficient Variational Inference for Sparse Deep Learning with Theoretical GuaranteeNeural Information Processing Systems (NeurIPS), 2020
Jincheng Bai
Qifan Song
Guang Cheng
BDL
231
49
0
15 Nov 2020
Non-exponentially weighted aggregation: regret bounds for unbounded loss
  functions
Non-exponentially weighted aggregation: regret bounds for unbounded loss functionsInternational Conference on Machine Learning (ICML), 2020
Pierre Alquier
577
21
0
07 Sep 2020
An Equivalence between Bayesian Priors and Penalties in Variational
  Inference
An Equivalence between Bayesian Priors and Penalties in Variational Inference
Pierre Wolinski
Guillaume Charpiat
Yann Ollivier
BDL
241
2
0
01 Feb 2020
Variable Selection with Rigorous Uncertainty Quantification using Deep
  Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises
  Phenomenon
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises PhenomenonInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Jeremiah Zhe Liu
BDL
300
10
0
03 Dec 2019
Asymptotic Consistency of Loss-Calibrated Variational Bayes
Asymptotic Consistency of Loss-Calibrated Variational Bayes
Prateek Jaiswal
Harsha Honnappa
Vinayak A. Rao
192
5
0
04 Nov 2019
Adaptive Variational Bayesian Inference for Sparse Deep Neural Network
Adaptive Variational Bayesian Inference for Sparse Deep Neural Network
Jincheng Bai
Qifan Song
Guang Cheng
BDL
226
2
0
10 Oct 2019
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean DiscrepancySymposium on Advances in Approximate Bayesian Inference (AABI), 2019
Badr-Eddine Chérief-Abdellatif
Pierre Alquier
309
82
0
29 Sep 2019
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