Fetching the paper…
Reading the bibliography…
Zeroth-order (derivative-free) optimization attracts a lot of attention in machine learning, because explicit gradient calculations may be computationally expensive or infeasible.
Least squares support vector machine classifiers
Johan AK Suykens and Joos Vandewalle · 1999
Earlier work this paper cites.
Learning structured prediction models: A large margin approach
Ben Taskar, Vassil Chatalbashev, Daphne Koller, and Carlos Guestrin · 2005
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael I Jordan · 2008
Earlier work this paper cites.
Statistical models: theory and practice
David A Freedman · 2009
Earlier work this paper cites.
Stochastic convex optimization with bandit feedback
Alekh Agarwal, Dean P Foster, Daniel J Hsu, Sham M Kakade, and Alexander Rakhlin · 2011
Earlier work this paper cites.
Random gradient-free minimization of convex functions
Yurii Nesterov and Vladimir Spokoiny · 2011
Earlier work this paper cites.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu · 2011
Earlier work this paper cites.
Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck and Nicolo Cesa-Bianchi · 2012
Earlier work this paper cites.
Randomized smoothing for stochastic optimization
John C Duchi, Peter L Bartlett, and Martin J Wainwright · 2012
Cited alongside, same era.
Query complexity of derivative-free optimization
Kevin G Jamieson, Robert Nowak, and Ben Recht · 2012
Cited alongside, same era.
A novel generalized ridge regression method for quantitative genetics
Xia Shen, Moudud Alam, Freddy Fikse, and Lars Rönnegård · 2013
Cited alongside, same era.
Parallel successive convex approximation for nonsmooth nonconvex optimization
Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, and Jong-Shi Pang · 2014
Cited alongside, same era.
Asynchronous parallel stochastic gradient for nonconvex optimization
Xiangru Lian, Yijun Huang, Yuncheng Li, and Ji Liu · 2015
Cited alongside, same era.
Asynchronous stochastic coordinate descent: Parallelism and convergence properties
Ji Liu and Stephen J Wright · 2015
On variance reduction in stochastic gradient descent and its asynchronous variants
Sashank J Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, and Alex J Smola · 2015
Later among the works it cites.
A stochastic quasi-newton method for large-scale optimization
Richard H Byrd, SL Hansen, Jorge Nocedal, and Yoram Singer · 2016
Closest in time.
Asynchronous stochastic gradient descent with variance reduction for non-convex optimization
Zhouyuan Huo and Heng Huang · 2016
Closest in time.
Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, and Ji Liu · 2016
Closest in time.
Fast stochastic methods for nonsmooth nonconvex optimization
Sashank J Reddi, Suvrit Sra, Barnabas Poczos, and Alex Smola · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Perturbed iterate analysis for asynchronous stochastic optimization
Horia Mania, Xinghao Pan, Dimitris Papailiopoulos, Benjamin Recht, Kannan Ramchandran, and Michael I Jordan · 2015
Cited alongside, same era.
Closest in time.
Asynchronous parallel greedy coordinate descent
Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S Dhillon, James Demmel, and Cho-Jui Hsieh · 2016
Closest in time.
Shen-Yi Zhao and Wu-Jun Li · 2016
Closest in time.