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A Variational Analysis of Stochastic Gradient Algorithms

A Variational Analysis of Stochastic Gradient Algorithms

8 February 2016
Stephan Mandt
Matthew D. Hoffman
David M. Blei
ArXiv (abs)PDFHTML

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
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
Thompson Sampling via Local Uncertainty
Zhendong Wang
Mingyuan Zhou
80
19
0
30 Oct 2019
Non-Gaussianity of Stochastic Gradient Noise
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Learning to Draw Samples with Amortized Stein Variational Gradient Descent
Yihao Feng
Dilin Wang
Qiang Liu
GANBDL
85
82
0
20 Jul 2017
Bridging the Gap between Constant Step Size Stochastic Gradient Descent
  and Markov Chains
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
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
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
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
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
Approximate Inference with Amortised MCMC
Yingzhen Li
Richard Turner
Qiang Liu
BDL
96
62
0
27 Feb 2017
Two Methods For Wild Variational Inference
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
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
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
Stochastic modified equations and adaptive stochastic gradient algorithms
Qianxiao Li
Cheng Tai
E. Weinan
77
285
0
19 Nov 2015
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