Fetching the paper…
Reading the bibliography…
We investigate the stochastic optimization problem of minimizing population risk, where the loss defining the risk is assumed to be weakly convex.
Proximité et dualité dans un espace hilbertien
J.-J. Moreau · 1965
Earlier work this paper cites.
Convex analysis
R. T. Rockafellar · 1970
Earlier work this paper cites.
The quasigradient method for the solving of the nonlinear programming problems
E. A. Nurminskii · 1973
Earlier work this paper cites.
Convergence de fonctions convexes, des sous-différentiels et semi-groupes associés
Hédy Attouch · 1977
Earlier work this paper cites.
On paraconvex multifunctions
S. Rolewicz · 1979
Earlier work this paper cites.
Favorable classes of Lipschitz-continuous functions in subgradient optimization
R.T. Rockafellar · 1982
Earlier work this paper cites.
Optimization and Nonsmooth Analysis
F.H. Clarke · 1983
Earlier work this paper cites.
Problem complexity and method efficiency in optimization
A.S. Nemirovsky and D.B. Yudin · 1983
Earlier work this paper cites.
Variational convergence for functions and operators
H. Attouch · 1984
Earlier work this paper cites.
On the method of bounded differences
Colin McDiarmid · 1989
Earlier work this paper cites.
Epigraphical processes: laws of large numbers for random LSC functions
Hédy Attouch and Roger J.-B. Wets · 1990
Earlier work this paper cites.
Entropic proximal mappings with applications to nonlinear programming
M. Teboulle · 1992
Earlier work this paper cites.
Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming
J. Eckstein · 1993
Earlier work this paper cites.
Asymptotic theory for solutions in statistical estimation and stochastic programming
Alan J. King and R. Tyrrell Rockafellar · 1993
Earlier work this paper cites.
On the asymptotics of constrained M M -estimation
Charles J. Geyer · 1994
Earlier work this paper cites.
Probabilistic bounds (via large deviations) for the solutions of stochastic programming problems
Yuri M. Kaniovski, Alan J. King, and Roger J-B Wets · 1995
Earlier work this paper cites.
Prox-regular functions in variational analysis
R.A. Poliquin and R.T. Rockafellar · 1996
Earlier work this paper cites.
Analysis of sample-path optimization
Stephen M. Robinson · 1996
Earlier work this paper cites.
Legendre functions and the method of random Bregman projections
H.H. Bauschke and J.M. Borwein · 1997
Earlier work this paper cites.
Variational Analysis
R.T. Rockafellar and R.J-B. Wets · 1998
Earlier work this paper cites.
Singularities of semiconcave functions in Banach spaces
P. Albano and P. Cannarsa · 1999
Earlier work this paper cites.
On the rate of convergence of optimal solutions of monte carlo approximations of stochastic programs
A. Shapiro and T. Homem-de Mello · 2000
Earlier work this paper cites.
On the asymptotics of constrained local M M -estimators
Alexander Shapiro · 2000
Earlier work this paper cites.
Stochastic programming by Monte Carlo simulation methods
Alexander Shapiro · 2000
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
Earlier work this paper cites.
Quantitative stability in stochastic programming: The method of probability metrics
Svetlozar T. Rachev and Werner Römisch · 2002
Earlier work this paper cites.
Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
Cited alongside, same era.
Local rademacher complexities
Peter L Bartlett, Olivier Bousquet, Shahar Mendelson, et al · 2005
Cited alongside, same era.
Stability results in learning theory
A. Rakhlin, S. Mukherjee, and T. Poggio · 2005
Cited alongside, same era.
Interior gradient and proximal methods for convex and conic optimization
A. Auslender and M. Teboulle · 2006
Cited alongside, same era.
Variational Analysis and Generalized Differentiation I: Basic Theory
B.S. Mordukhovich · 2006
Cited alongside, same era.
Stability of ε \varepsilon -approximate solutions to convex stochastic programs
W. Römisch and R. Wets · 2007
Cited alongside, same era.
Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
S. Ghadimi, G. Lan, and H. Zhang · 2016
Later among the works it cites.
Fast rates for general unbounded loss functions: from ERM to generalized Bayes
Peter D Grünwald and Nishant A Mehta · 2016
Later among the works it cites.
Relatively-smooth convex optimization by first-order methods, and applications
H. Lu, R. Freund, and Y. Nesterov · 2016
Later among the works it cites.
A vector-contraction inequality for rademacher complexities
Andreas Maurer · 2016
Later among the works it cites.
Fast rates with high probability in exp-concave statistical learning
Nishant A Mehta · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Online learning: Theory, algorithms, and applications
S. Shalev-Shwartz · 2007
Cited alongside, same era.
Uniform laws of large numbers for set-valued mappings and subdifferentials of random functions
Alexander Shapiro and Huifu Xu · 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.
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Sham M Kakade, Karthik Sridharan, and Ambuj Tewari · 2009
Cited alongside, same era.
Stochastic Convex Optimization
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2009
Cited alongside, same era.
Fast rates for regularized objectives
Karthik Sridharan, Shai Shalev-shwartz, and Nathan Srebro · 2009
Cited alongside, same era.
A descent lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications
H.H. Bauschke, J. Bolte, and M. Teboulle · 2017
Later among the works it cites.
Regularizing with Bregman-Moreau envelopes
H.H. Bauschke, M.N. Dao, and S.B. Lindstrom · 2017
Later among the works it cites.
Proximally guided stochastic method for nonsmooth, nonconvex problems
D. Davis and B. Grimmer · 2017
Later among the works it cites.
The nonsmooth landscape of phase retrieval
Damek Davis, Dmitriy Drusvyatskiy, and Courtney Paquette · 2017
Later among the works it cites.
Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval
J.C. Duchi and F. Ruan · 2017
Later among the works it cites.
Stochastic methods for composite optimization problems
J.C. Duchi and F. Ruan · 2017
Later among the works it cites.
H. Lu · 2017
Later among the works it cites.
Asymptotic and finite-sample properties of estimators based on stochastic gradients
P. Toulis and E.M. Airoldi · 2017
Later among the works it cites.
Stochastic model-based minimization of weakly convex functions
D. Davis and D. Drusvyatskiy · 2018
Closest in time.
Stochastic subgradient method converges on tame functions
D. Davis, D. Drusvyatskiy, S.M. Kakade, and J.D. Lee · 2018
Closest in time.
Stochastic model-based minimization under high-order growth
D. Davis, D. Drusvyatskiy, and K.J. MacPhee · 2018
Closest in time.
Subgradient Methods for Sharp Weakly Convex Functions
Damek Davis, Dmitriy Drusvyatskiy, Kellie J. MacPhee, and Courtney Paquette · 2018
Closest in time.
The proximal point method revisited
D. Drusvyatskiy · 2018
Closest in time.
Efficiency of minimizing compositions of convex functions and smooth maps
D. Drusvyatskiy and C. Paquette · 2018
Closest in time.
Uniform convergence of gradients for non-convex learning and optimization
Dylan Foster, Ayush Sekhari, and Karthik Sridharan · 2018
Closest in time.
Nonconvex robust low-rank matrix recovery
X. Li, Z. Zhihui, A.M.-C So, and R. Vidal · 2018
Closest in time.
Fast rates of erm and stochastic approximation: Adaptive to error bound conditions
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, and Tianbao Yang · 2018
Closest in time.
The landscape of empirical risk for nonconvex losses
Song Mei, Yu Bai, and Andrea Montanari · 2018
Closest in time.
A simplified view of first order methods for optimization
M. Teboulle · 2018
Closest in time.
High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
Closest in time.
On the convergence rate of stochastic mirror descent for nonsmooth nonconvex optimization
S. Zhang and N. He · 2018
Closest in time.