Fetching the paper…
Reading the bibliography…
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
Function minimization by conjugate gradients
R. Fletcher and C.M. Reeves · 1964
Earlier work this paper cites.
Minimization of functions having Lipschitz continuous first partial derivatives
L. Armijo · 1966
Earlier work this paper cites.
A new double-rank minimization algorithm
C.G. Broyden · 1969
Earlier work this paper cites.
Convergence conditions for ascent methods
P. Wolfe · 1969
Earlier work this paper cites.
A new approach to variable metric algorithms
R. Fletcher · 1970
Earlier work this paper cites.
A family of variable metric updates derived by variational means
D. Goldfarb · 1970
Earlier work this paper cites.
Conditioning of quasi-Newton methods for function minimization
D.F. Shanno · 1970
Earlier work this paper cites.
The Geometry of Random Fields
R.J. Adler · 1981
Earlier work this paper cites.
Spline models for observational data
G. Wahba · 1990
Earlier work this paper cites.
On the computation of the bivariate normal integral
Z. Drezner and G.O. Wesolowsky · 1990
Cited alongside, same era.
Probability, Random Variables, and Stochastic Processes
A. Papoulis · 1991
Cited alongside, same era.
Efficient global optimization of expensive black-box functions
D.R. Jones, M. Schonlau, and W.J. Welch · 1998
Cited alongside, same era.
Local gain adaptation in stochastic gradient descent
N.N. Schraudolph · 1999
Cited alongside, same era.
Numerical Optimization
J. Nocedal and S.J. Wright · 1999
Cited alongside, same era.
Adaptive method of realizing natural gradient learning for multilayer perceptrons
S.-I. Amari, H. Park, and K. Fukumizu · 2000
Cited alongside, same era.
A fast natural Newton method
N.L. Roux and A.W. Fitzgibbon · 2010
Later among the works it cites.
Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2011
Later among the works it cites.
Fast variational inference in the conjugate exponential family
J. Hensman, M. Rattray, and N.D. Lawrence · 2012
Later among the works it cites.
Stochastic variational inference
M.D. Hoffman, D.M. Blei, C. Wang, and J. Paisley · 2013
Later among the works it cites.
Streaming variational Bayes
T. Broderick, N. Boyd, A. Wibisono, A.C. Wilson, and M.I. Jordan · 2013
Later among the works it cites.
An adaptive learning rate for stochastic variational inference
R. Rajesh, W. Chong, D. Blei, and E. Xing · 2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Solving large scale linear prediction problems using stochastic gradient descent algorithms
T. Zhang · 2004
Cited alongside, same era.
Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming
A.P. George and W.B. Powell · 2006
Cited alongside, same era.
Gaussian Processes for Machine Learning
C.E. Rasmussen and C.K.I. Williams · 2006
Cited alongside, same era.
Large-scale machine learning with stochastic gradient descent
L. Bottou · 2010
Cited alongside, same era.
Fast Probabilistic Optimization from Noisy Gradients
P. Hennig · 2013
Later among the works it cites.
No more pesky learning rates
T. Schaul, S. Zhang, and Y. LeCun · 2013
Later among the works it cites.
Bayesian filtering and smoothing
S. Särkkä · 2013
Later among the works it cites.