Stochastic adversarial noise in the ``black box optimization problem
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Publication:6588731
DOI10.1007/978-3-031-47859-8_5MaRDI QIDQ6588731
Publication date: 16 August 2024
Derivative-free methods and methods using generalized derivatives (90C56) Stochastic programming (90C15)
Cites Work
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- Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
- Optimal order of accuracy of search algorithms in stochastic optimization
- Gradient-free two-point methods for solving stochastic nonsmooth convex optimization problems with small non-random noises
- An accelerated directional derivative method for smooth stochastic convex optimization
- Noisy zeroth-order optimization for non-smooth saddle point problems
- A theoretical and empirical comparison of gradient approximations in derivative-free optimization
- Zeroth-order methods for noisy Hölder-gradient functions
- Stochastic online optimization. Single-point and multi-point non-linear multi-armed bandits. Convex and strongly-convex case
- Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations
- Introduction to Derivative-Free Optimization
- Derivative-Free and Blackbox Optimization
- Gradient-Free Methods with Inexact Oracle for Convex-Concave Stochastic Saddle-Point Problem
- Distributed Randomized Gradient-Free Mirror Descent Algorithm for Constrained Optimization
- An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization
- Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
- An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback
- Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
- Stochastic Estimation of the Maximum of a Regression Function
- Gradient-free federated learning methods with \(l_1\) and \(l_2\)-randomization for non-smooth convex stochastic optimization problems
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