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Learning from time-dependent streaming data with online stochastic
  algorithms

Learning from time-dependent streaming data with online stochastic algorithms

25 May 2022
Antoine Godichon-Baggioni
Nicklas Werge
Olivier Wintenberger
ArXivPDFHTML

Papers citing "Learning from time-dependent streaming data with online stochastic algorithms"

14 / 14 papers shown
Title
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates
  of Linear Stochastic Approximation
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation
Alain Durmus
Eric Moulines
A. Naumov
S. Samsonov
89
25
0
10 Jul 2022
Stochastic Online Convex Optimization. Application to probabilistic time
  series forecasting
Stochastic Online Convex Optimization. Application to probabilistic time series forecasting
Olivier Wintenberger
AI4TS
65
6
0
01 Feb 2021
On the asymptotic rate of convergence of Stochastic Newton algorithms
  and their Weighted Averaged versions
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions
Claire Boyer
Antoine Godichon-Baggioni
44
19
0
19 Nov 2020
Linearly Converging Error Compensated SGD
Linearly Converging Error Compensated SGD
Eduard A. Gorbunov
D. Kovalev
Dmitry Makarenko
Peter Richtárik
202
79
0
23 Oct 2020
Better Theory for SGD in the Nonconvex World
Better Theory for SGD in the Nonconvex World
Ahmed Khaled
Peter Richtárik
74
183
0
09 Feb 2020
Introduction to Online Convex Optimization
Introduction to Online Convex Optimization
Elad Hazan
OffRL
172
1,929
0
07 Sep 2019
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Belhal Karimi
B. Miasojedow
Eric Moulines
Hoi-To Wai
63
91
0
02 Feb 2019
New Convergence Aspects of Stochastic Gradient Algorithms
New Convergence Aspects of Stochastic Gradient Algorithms
Lam M. Nguyen
Phuong Ha Nguyen
Peter Richtárik
K. Scheinberg
Martin Takáč
Marten van Dijk
108
66
0
10 Nov 2018
A Stochastic Quasi-Newton Method for Large-Scale Optimization
A Stochastic Quasi-Newton Method for Large-Scale Optimization
R. Byrd
Samantha Hansen
J. Nocedal
Y. Singer
ODL
108
471
0
27 Jan 2014
Non-strongly-convex smooth stochastic approximation with convergence
  rate O(1/n)
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
Francis R. Bach
Eric Moulines
89
405
0
10 Jun 2013
Online Learning for Time Series Prediction
Online Learning for Time Series Prediction
Oren Anava
Elad Hazan
Shie Mannor
Ohad Shamir
AI4TS
62
148
0
27 Feb 2013
ADADELTA: An Adaptive Learning Rate Method
ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
ODL
152
6,624
0
22 Dec 2012
Convergence Rates of Inexact Proximal-Gradient Methods for Convex
  Optimization
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
Mark Schmidt
Nicolas Le Roux
Francis R. Bach
205
582
0
12 Sep 2011
A fast and recursive algorithm for clustering large datasets with
  $k$-medians
A fast and recursive algorithm for clustering large datasets with kkk-medians
H. Cardot
P. Cénac
J. Monnez
87
64
0
21 Jan 2011
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