Fetching the paper…
Reading the bibliography…
Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic optimization problems which arise in machine learning.
A general class of exponential inequalities for martingales and ratios
De La Peña, V.H · 1999
Earlier work this paper cites.
Stochastic Approximation and Recursive Algorithms and Applications
Kushner, H. and Yin, G · 2003
Earlier work this paper cites.
Training linear SVMs in linear time
Joachims, T · 2006
Earlier work this paper cites.
Logarithmic regret algorithms for online convex optimization
Hazan, E., Agarwal, A., and Kale, S · 2007
Earlier work this paper cites.
High-probability regret bounds for bandit online linear optimization
Bartlett, P.L., Dani, V., Hayes, T., Kakade, S., Rakhlin, A., and Tewari, A · 2008
Cited alongside, same era.
Robust stochastic approximation approach to stochastic programming
Nemirovski, A., Juditsky, A., Lan, G., and Shapiro, A · 2009
Cited alongside, same era.
Stochastic convex optimization
Shalev-Shwartz, S., Shamir, O., Srebro, N., and Sridharan, K · 2009
Cited alongside, same era.
Primal-dual subgradient methods for minimizing uniformly convex functions
Juditsky, A. and Nesterov, Y · 2010
Cited alongside, same era.
Non-asymptotic analysis of stochastic approximation algorithms for machine learning
Bach, F. and Moulines, E · 2011
Closest in time.
Beyond the regret minimization barrier: An optimal algorithm for stochastic strongly-convex optimization
Hazan, E. and Kale, S · 2011
Closest in time.
Pegasos: primal estimated sub-gradient solver for svm
Shalev-Shwartz, S., Singer, Y., Srebro, N., and Cotter, A · 2011
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…