Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization

IJCAI, pp. 2549-2555, 2019.

Cited by: 1|Bibtex|Views17|DOI:https://doi.org/10.24963/ijcai.2019/354
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Alternating direction method of multipliers (ADMM) is a popular optimization tool for the composite and constrained problems in machine learning. However, in many machine learning problems such as black-box attacks and bandit feedback, ADMM could fail because the explicit gradients of these problems are difficult or infeasible to obtain...More

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