TY - GEN
T1 - Stochastic variance reduced optimization for nonconvex sparse learning
AU - Li, Xingguo
AU - Zhao, Tuo
AU - Arora, Raman
AU - Liu, Han
AU - Haupt, Jarvis
N1 - Funding Information:
This research is supported by NSF CCF-1217751; NSF AST-1247885; DARPA Young Faculty Award N66001-14-1-4047; NSF DMS-1454377-CAREER; NSF IIS-1546482-BIGDATA; NIH R01MH102339; NSF IIS-1408910; NSFIIS-1332109; NIH R01GM083084.
PY - 2016
Y1 - 2016
N2 - We propose a stochastic variance reduced optimization algorithm for solving a class of largescale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. Numerical experiments demonstrate the efficiency of our method in terms of both parameter estimation and computational performance.
AB - We propose a stochastic variance reduced optimization algorithm for solving a class of largescale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. Numerical experiments demonstrate the efficiency of our method in terms of both parameter estimation and computational performance.
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M3 - Conference contribution
AN - SCOPUS:84998812114
T3 - 33rd International Conference on Machine Learning, ICML 2016
SP - 1448
EP - 1460
BT - 33rd International Conference on Machine Learning, ICML 2016
A2 - Balcan, Maria Florina
A2 - Weinberger, Kilian Q.
PB - International Machine Learning Society (IMLS)
T2 - 33rd International Conference on Machine Learning, ICML 2016
Y2 - 19 June 2016 through 24 June 2016
ER -