Fetching the paper…
Reading the bibliography…
Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares defined by an incremental gradient method.
The regularization properties of adjoint gradient method in ill-posed problems
A. Nemirovskii · 1986
Earlier work this paper cites.
Introduction to Optimization
B. Polyak · 1987
Earlier work this paper cites.
Optimum bounds for the distributions of martingales in Banach spaces
I. Pinelis · 1994
Earlier work this paper cites.
Regularization of inverse problems
H. W. Engl, M. Hanke, and A. Neubauer · 1996
Earlier work this paper cites.
A new class of incremental gradient methods for least squares problems
D. P. Bertsekas · 1997
Earlier work this paper cites.
Efficient backprop
Y. LeCun, L. Bottou, G. Orr, and K. Muller · 1998
Earlier work this paper cites.
Statistical learning theory
V.N. Vapnik · 1998
Earlier work this paper cites.
Incremental subgradient methods for nondifferentiable optimization
A. Nedic and D. P Bertsekas · 2001
Earlier work this paper cites.
Boosting with the l2 loss: Regression and classification
P. Buhlmann and B. Yu · 2003
Earlier work this paper cites.
On the generalization ability of on-line learning algorithms
N. Cesa-Bianchi, A. Conconi, and C. Gentile · 2004
Earlier work this paper cites.
Some properties of regularized kernel methods
E. De Vito, L. Rosasco, A. Caponnetto, M. Piana, and A. Verri · 2004
Earlier work this paper cites.
Learning from examples as an inverse problem
E. De Vito, L. Rosasco, A. Caponnetto, U. De Giovannini, and F. Odone · 2005
Cited alongside, same era.
Functional Data Analysis
J. Ramsay and B. Silverman · 2005
Cited alongside, same era.
Shannon sampling II: Connections to learning theory
S. Smale and D. Zhou · 2005
Cited alongside, same era.
Optimal rates for regularized least-squares algorithm
A. Caponnetto and E. De Vito · 2006
Cited alongside, same era.
Prediction, learning, and games
N. Cesa-Bianchi and G. Lugosi · 2006
Cited alongside, same era.
On regularization algorithms in learning theory
F. Bauer, S. Pereverzev, and L. Rosasco · 2007
Cited alongside, same era.
Learning Theory: An Approximation Theory Viewpoint
Online gradient descent learning algorithms
Y. Ying and M. Pontil · 2008
Later among the works it cites.
Optimal rates for regularized least squares regression
I. Steinwart, D. R. Hush, and C. Scovel · 2009
Later among the works it cites.
Optimal learning rates for kernel conjugate gradient regression
G. Blanchard and N. Krämer · 2010
Later among the works it cites.
Adaptive rates for regularization operators in learning theory
A. Caponnetto and Yuan Yao · 2010
Later among the works it cites.
The tradeoffs of large scale learning
L. Bottou and O. Bousquet · 2011
Later among the works it cites.
Early stopping for non-parametric regression: An optimal data-dependent stopping rule
G. Raskutti, M. Wainwright, and B. Yu · 2011
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Cucker and D. X. Zhou · 2007
Cited alongside, same era.
Learning theory estimates via integral operators and their approximations
S. Smale and D.-X. Zhou · 2007
Cited alongside, same era.
On early stopping in gradient descent learning
Y. Yao, L. Rosasco, and A. Caponnetto · 2007
Cited alongside, same era.
Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2008
Cited alongside, same era.
Support Vector Machines
I. Steinwart and A. Christmann · 2008
Cited alongside, same era.
N. Srebro, K. Sridharan, and A. Tewari · 2012
Later among the works it cites.
Non-parametric stochastic approximation with large step sizes
F. Bach and A. Dieuleveut · 2014
Closest in time.
Kernel methods match deep neural networks on timit
P.-S. Huang, H. Avron, T. Sainath, V. Sindhwani, and B. Ramabhadran · 2014
Closest in time.
Simultaneous model selection and optimization through parameter-free stochastic learning
F. Orabona · 2014
Closest in time.
Online learning as stochastic approximation of regularization paths: optimality and almost-sure convergence
P. Tarrès and Y. Yao · 2014
Closest in time.