1 citations · 2 across the 9 of their papers we have counts for
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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…
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,…
Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation
Carolin Lawrence, Artem Sokolov, Stefan Riezler
The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that…