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stat.ML2020
Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization
Mayumi Ohta, Nathaniel Berger, Artem Sokolov +1
Interest in stochastic zeroth-order (SZO) methods has recently been revived in black-box optimization scenarios such as adversarial black-box attacks to deep neural networks. SZO m…
stat.ML2018
Sparse Stochastic Zeroth-Order Optimization with an Application to Bandit Structured Prediction
Artem Sokolov, Julian Hitschler, Mayumi Ohta +1
Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however,…