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Stochastic convex optimization is a basic and well studied primitive in machine learning.
A general method of solving extremum problems
B. T. Polyak · 1967
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The minimization of quasicomplex functionals
Y. I. Zabotin, A. Korablev, and R. F. Khabibullin · 1972
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A method to find a point of a convex set
R. F. Khabibullin · 1977
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Minimization methods for nonsmooth convex and quasiconvex functions
Y. E. Nesterov · 1984
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Price discrimination and social welfare
H. R. Varian · 1985
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Quasi subgradient algorithms for calculating surrogate constraints
J. Sikorski · 1986
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Generalised linear models
P. McCullagh and J. Nelder · 1989
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Bifurcations of recurrent neural networks in gradient descent learning
K. Doya · 1993
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Exponentially many local minima for single neurons
P. Auer, M. Herbster, and M. K. Warmuth · 1996
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Complexity analysis of an interior cutting plane method for convex feasibility problems
J.-L. Goffin, Z.-Q. Luo, and Y. Ye · 1996
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Topics in microeconomics: Industrial organization, auctions, and incentives
E. Wolfstetter · 1999
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Convergence and efficiency of subgradient methods for quasiconvex minimization
K. C. Kiwiel · 2001
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On convergence properties of a subgradient method
I. V. Konnov · 2003
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Quasiconvex optimization for robust geometric reconstruction
Q. Ke and T. Kanade · 2007
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Learning deep architectures for AI
Y. Bengio · 2009
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The isotron algorithm: High-dimensional isotonic regression
A. T. Kalai and R. Sastry · 2009
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The theory of incentives: the principal-agent model
J.-J. Laffont and D. Martimort · 2009
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Learning recurrent neural networks with hessian-free optimization
J. Martens and I. Sutskever · 2011
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On the difficulty of training recurrent neural networks
R. Pascanu, T. Mikolov, and Y. Bengio · 2013
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Convex optimization
S. Boyd and L. Vandenberghe · 2004
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
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