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1602.02666
Cited By
A Variational Analysis of Stochastic Gradient Algorithms
8 February 2016
Stephan Mandt
Matthew D. Hoffman
David M. Blei
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Papers citing
"A Variational Analysis of Stochastic Gradient Algorithms"
36 / 86 papers shown
Title
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
Umut Simsekli
Mert Gurbuzbalaban
T. H. Nguyen
G. Richard
Levent Sagun
88
59
0
29 Nov 2019
Thompson Sampling via Local Uncertainty
Zhendong Wang
Mingyuan Zhou
80
19
0
30 Oct 2019
Non-Gaussianity of Stochastic Gradient Noise
A. Panigrahi
Raghav Somani
Navin Goyal
Praneeth Netrapalli
68
53
0
21 Oct 2019
Stochastic Gradient and Langevin Processes
Xiang Cheng
Dong Yin
Peter L. Bartlett
Michael I. Jordan
82
5
0
07 Jul 2019
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
T. H. Nguyen
Umut Simsekli
Mert Gurbuzbalaban
G. Richard
79
65
0
21 Jun 2019
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Hugo Penedones
C. Riquelme
Damien Vincent
Hartmut Maennel
Timothy A. Mann
André Barreto
Sylvain Gelly
Gergely Neu
OffRL
46
10
0
19 Jun 2019
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Théo Galy-Fajou
F. Wenzel
Christian Donner
Manfred Opper
60
30
0
23 May 2019
Distribution-Dependent Analysis of Gibbs-ERM Principle
Ilja Kuzborskij
Nicolò Cesa-Bianchi
Csaba Szepesvári
85
20
0
05 Feb 2019
Quantitative Weak Convergence for Discrete Stochastic Processes
Xiang Cheng
Peter L. Bartlett
Michael I. Jordan
43
5
0
03 Feb 2019
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent
Wenqing Hu
Zhanxing Zhu
Haoyi Xiong
Jun Huan
MLT
54
10
0
18 Jan 2019
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
Umut Simsekli
Levent Sagun
Mert Gurbuzbalaban
114
252
0
18 Jan 2019
A continuous-time analysis of distributed stochastic gradient
Nicholas M. Boffi
Jean-Jacques E. Slotine
46
15
0
28 Dec 2018
Information-Directed Exploration for Deep Reinforcement Learning
Nikolay Nikolov
Johannes Kirschner
Felix Berkenkamp
Andreas Krause
67
72
0
18 Dec 2018
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
Qianxiao Li
Cheng Tai
E. Weinan
124
150
0
05 Nov 2018
Continuous-time Models for Stochastic Optimization Algorithms
Antonio Orvieto
Aurelien Lucchi
119
32
0
05 Oct 2018
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
C. J. Li
Mengdi Wang
Han Liu
Tong Zhang
82
12
0
29 Aug 2018
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
C. J. Li
Zhaoran Wang
Han Liu
DiffM
113
18
0
29 Aug 2018
PCA of high dimensional random walks with comparison to neural network training
J. Antognini
Jascha Narain Sohl-Dickstein
OOD
62
29
0
22 Jun 2018
Differential Equations for Modeling Asynchronous Algorithms
Li He
Qi Meng
Wei-neng Chen
Zhiming Ma
Tie-Yan Liu
52
9
0
08 May 2018
Scalable Generalized Dynamic Topic Models
P. Jähnichen
F. Wenzel
Marius Kloft
Stephan Mandt
BDL
107
40
0
21 Mar 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
C. Riquelme
George Tucker
Jasper Snoek
BDL
93
366
0
26 Feb 2018
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
F. Wenzel
Théo Galy-Fajou
Christian Donner
Marius Kloft
Manfred Opper
105
36
0
18 Feb 2018
Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms
Yazhen Wang
93
17
0
27 Nov 2017
Advances in Variational Inference
Cheng Zhang
Judith Butepage
Hedvig Kjellström
Stephan Mandt
BDL
233
698
0
15 Nov 2017
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Pratik Chaudhari
Stefano Soatto
MLT
104
304
0
30 Oct 2017
Learning to Draw Samples with Amortized Stein Variational Gradient Descent
Yihao Feng
Dilin Wang
Qiang Liu
GAN
BDL
85
82
0
20 Jul 2017
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
Aymeric Dieuleveut
Alain Durmus
Francis R. Bach
108
156
0
20 Jul 2017
Bayesian Nonlinear Support Vector Machines for Big Data
F. Wenzel
Théo Galy-Fajou
M. Deutsch
Marius Kloft
BDL
73
27
0
18 Jul 2017
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI
Justin Domke
BDL
80
6
0
20 Jun 2017
Statistical inference using SGD
Tianyang Li
Liu Liu
Anastasios Kyrillidis
Constantine Caramanis
FedML
58
38
0
21 May 2017
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang
Erik Learned-Miller
Andrew McCallum
100
355
0
24 Apr 2017
Approximate Inference with Amortised MCMC
Yingzhen Li
Richard Turner
Qiang Liu
BDL
96
62
0
27 Feb 2017
Two Methods For Wild Variational Inference
Qiang Liu
Yihao Feng
BDL
138
19
0
30 Nov 2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Pratik Chaudhari
A. Choromańska
Stefano Soatto
Yann LeCun
Carlo Baldassi
C. Borgs
J. Chayes
Levent Sagun
R. Zecchina
ODL
118
775
0
06 Nov 2016
Patterns of Scalable Bayesian Inference
E. Angelino
Matthew J. Johnson
Ryan P. Adams
107
87
0
16 Feb 2016
Stochastic modified equations and adaptive stochastic gradient algorithms
Qianxiao Li
Cheng Tai
E. Weinan
77
285
0
19 Nov 2015
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