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1402.2365
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On perturbed proximal gradient algorithms
11 February 2014
Yves Atchadé
G. Fort
Eric Moulines
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Papers citing
"On perturbed proximal gradient algorithms"
45 / 45 papers shown
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm
James Cuin
Davide Carbone
O. Deniz Akyildiz
277
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23 May 2025
Proximal Interacting Particle Langevin Algorithms
Paula Cordero Encinar
F. R. Crucinio
O. Deniz Akyildiz
325
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20 Jun 2024
A Multiscale Perspective on Maximum Marginal Likelihood Estimation
O. Deniz Akyildiz
M. Ottobre
I. Souttar
263
6
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06 Jun 2024
EMC
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2
: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence
Chung-Yiu Yau
Hoi-To Wai
Parameswaran Raman
Soumajyoti Sarkar
Mingyi Hong
188
1
0
16 Apr 2024
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion
Anna Korba
Peter Bartlett
Mathieu Blondel
Valentin De Bortoli
Arnaud Doucet
Felipe Llinares-López
Courtney Paquette
Quentin Berthet
435
19
0
08 Feb 2024
Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability
Céline Comte
Matthieu Jonckheere
J. Sanders
Albert Senen-Cerda
235
2
0
05 Dec 2023
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization
Junwen Qiu
Xiao Li
Andre Milzarek
583
3
0
02 Dec 2023
Weight-Sharing Regularization
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Mehran Shakerinava
Motahareh Sohrabi
Siamak Ravanbakhsh
Damien Scieur
293
1
0
06 Nov 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space
International Conference on Machine Learning (ICML), 2023
Michael Diao
Krishnakumar Balasubramanian
Sinho Chewi
Adil Salim
BDL
160
36
0
10 Apr 2023
Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation
E S A I M: Probability & Statistics (ESAIM-PS), 2023
Ö. Deniz Akyildiz
F. R. Crucinio
Mark Girolami
Tim Johnston
Sotirios Sabanis
278
19
0
23 Mar 2023
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
Tian-cheng Li
Fan Chen
Huajie Chen
Zaiwen Wen
192
5
0
19 Mar 2023
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
IEEE Transactions on Signal Processing (IEEE TSP), 2023
Hadrien Hendrikx
G. Fort
Eric Moulines
Hoi-To Wai
252
16
0
22 Feb 2023
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization
Statistics and computing (Stat. Comput.), 2023
G. Fort
Eric Moulines
258
7
0
02 Jan 2023
The Stochastic Proximal Distance Algorithm
Statistics and computing (Stat. Comput.), 2022
Hao Jiang
Jason Xu
256
0
0
21 Oct 2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Neural Information Processing Systems (NeurIPS), 2022
Puyu Wang
Yunwen Lei
Yiming Ying
Ding-Xuan Zhou
276
21
0
16 Sep 2022
A Unified Convergence Theorem for Stochastic Optimization Methods
Neural Information Processing Systems (NeurIPS), 2022
Xiao Li
Andre Milzarek
234
18
0
08 Jun 2022
FedPop: A Bayesian Approach for Personalised Federated Learning
Neural Information Processing Systems (NeurIPS), 2022
Nikita Kotelevskii
Maxime Vono
Eric Moulines
Alain Durmus
FedML
187
50
0
07 Jun 2022
Gradient flows and randomised thresholding: sparse inversion and classification
Inverse Problems (IP), 2022
J. Latz
171
2
0
22 Mar 2022
Sharper Bounds for Proximal Gradient Algorithms with Errors
SIAM Journal on Optimization (SIAM J. Optim.), 2022
Anis Hamadouche
Yun-Shun Wu
Andrew M. Wallace
João F. C. Mota
157
8
0
04 Mar 2022
Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach
INFORMS journal on computing (IJOC), 2022
T. Wan
L. Hong
86
18
0
15 Jan 2022
On the accept-reject mechanism for Metropolis-Hastings algorithms
N. Glatt-Holtz
J. Krometis
Cecilia F. Mondaini
300
12
0
09 Nov 2020
Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework
Siliang Zhang
Yunxiao Chen
201
24
0
17 Aug 2020
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis
Valentin De Bortoli
Alain Durmus
A. F. Vidal
Marcelo Pereyra
203
22
0
13 Aug 2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
Adil Salim
Peter Richtárik
221
43
0
16 Jun 2020
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
International Conference on Learning Representations (ICLR), 2020
Erik Nijkamp
Ruiqi Gao
Pavel Sountsov
Srinivas Vasudevan
Bo Pang
Song-Chun Zhu
Ying Nian Wu
BDL
199
24
0
12 Jun 2020
An Analysis of the Adaptation Speed of Causal Models
Rémi Le Priol
Reza Babanezhad Harikandeh
Yoshua Bengio
Damien Scieur
CML
240
15
0
18 May 2020
High-Performance Statistical Computing in the Computing Environments of the 2020s
Statistical Science (Statist. Sci.), 2020
Seyoon Ko
Hua Zhou
Jin J. Zhou
Joong-Ho Won
410
9
0
07 Jan 2020
Distributed Fixed Point Methods with Compressed Iterates
Sélim Chraibi
Ahmed Khaled
D. Kovalev
Peter Richtárik
Adil Salim
Martin Takávc
FedML
148
19
0
20 Dec 2019
Maximum entropy methods for texture synthesis: theory and practice
SIAM Journal on Mathematics of Data Science (SIMODS), 2019
Valentin De Bortoli
A. Desolneux
Alain Durmus
B. Galerne
Arthur Leclaire
GAN
214
5
0
03 Dec 2019
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments
A. F. Vidal
Valentin De Bortoli
Marcelo Pereyra
Alain Durmus
464
7
0
26 Nov 2019
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Statistics and computing (Stat. Comput.), 2019
Valentin De Bortoli
Alain Durmus
Marcelo Pereyra
A. F. Vidal
285
38
0
28 Jun 2019
Macrocanonical Models for Texture Synthesis
Valentin De Bortoli
A. Desolneux
B. Galerne
Arthur Leclaire
133
3
0
12 Apr 2019
Forward-backward-forward methods with variance reduction for stochastic variational inequalities
R. Boț
P. Mertikopoulos
Mathias Staudigl
P. Vuong
156
23
0
09 Feb 2019
A Fully Stochastic Primal-Dual Algorithm
Pascal Bianchi
W. Hachem
Adil Salim
140
6
0
23 Jan 2019
A probabilistic incremental proximal gradient method
Ömer Deniz Akyildiz
Émilie Chouzenoux
Victor Elvira
Joaquín Míguez
164
3
0
04 Dec 2018
Approximate Newton-based statistical inference using only stochastic gradients
Tianyang Li
Anastasios Kyrillidis
Liu Liu
Constantine Caramanis
184
7
0
23 May 2018
Analysis of nonsmooth stochastic approximation: the differential inclusion approach
Szymon Majewski
B. Miasojedow
Eric Moulines
119
51
0
04 May 2018
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization
Andre Milzarek
X. Xiao
Shicong Cen
Zaiwen Wen
M. Ulbrich
166
37
0
09 Mar 2018
DropLasso: A robust variant of Lasso for single cell RNA-seq data
Beyrem Khalfaoui
Jean-Philippe Vert
132
8
0
26 Feb 2018
A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
Computational optimization and applications (COA), 2017
L. Briceño-Arias
Giovanni Chierchia
Émilie Chouzenoux
J. Pesquet
176
29
0
25 Dec 2017
Optimization Methods for Large-Scale Machine Learning
Léon Bottou
Frank E. Curtis
J. Nocedal
822
3,554
0
15 Jun 2016
Convergence of Contrastive Divergence Algorithm in Exponential Family
Bai Jiang
Tung-Yu Wu
Yifan Jin
W. Wong
157
12
0
17 Mar 2016
SGD with Variance Reduction beyond Empirical Risk Minimization
M. Achab
Agathe Guilloux
Stéphane Gaïffas
Emmanuel Bacry
268
5
0
16 Oct 2015
A SAEM Algorithm for Fused Lasso Penalized Non Linear Mixed Effect Models: Application to Group Comparison in Pharmacokinetic
E. Ollier
Adeline M. M. Samson
X. Delavenne
V. Viallon
253
5
0
26 Feb 2015
RAPID: Rapidly Accelerated Proximal Gradient Algorithms for Convex Minimization
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014
Ziming Zhang
Venkatesh Saligrama
217
9
0
13 Jun 2014
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