Pages that link to "Item:Q5361319"

The following pages link to An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback (Q5361319):

Displaying 35 items.

• An accelerated directional derivative method for smooth stochastic convex optimization (Q2029381) (← links)
• Improved regret for zeroth-order adversarial bandit convex optimisation (Q2035748) (← links)
• A new one-point residual-feedback oracle for black-box learning and control (Q2063773) (← links)
• Distributed online bandit optimization under random quantization (Q2097746) (← links)
• Noisy zeroth-order optimization for non-smooth saddle point problems (Q2104286) (← links)
• One-point gradient-free methods for smooth and non-smooth saddle-point problems (Q2117626) (← links)
• A theoretical and empirical comparison of gradient approximations in derivative-free optimization (Q2143221) (← links)
• Zeroth-order algorithms for stochastic distributed nonconvex optimization (Q2151863) (← links)
• Smoothed functional-based gradient algorithms for off-policy reinforcement learning: a non-asymptotic viewpoint (Q2242923) (← links)
• On the upper bound for the expectation of the norm of a vector uniformly distributed on the sphere and the phenomenon of concentration of uniform measure on the sphere (Q2282831) (← links)
• Zeroth-order feedback optimization for cooperative multi-agent systems (Q2682294) (← links)
• (Q4637066) (← links)
• Gradient-Free Methods with Inexact Oracle for Convex-Concave Stochastic Saddle-Point Problem (Q4965105) (← links)
• Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems (Q4969058) (← links)
• (Q4999102) (← links)
• New First-Order Algorithms for Stochastic Variational Inequalities (Q5051379) (← links)
• Finite Difference Gradient Approximation: To Randomize or Not? (Q5057983) (← links)
• An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization (Q5081777) (← links)
• Technical Note—Nonstationary Stochastic Optimization Under <i>L</i>_<i>p,q</i>-Variation Measures (Q5129221) (← links)
• Derivative-free optimization methods (Q5230522) (← links)
• Interior-Point Methods for Full-Information and Bandit Online Learning (Q5271795) (← links)
• Gradient-free federated learning methods with \(l_1\) and \(l_2\)-randomization for non-smooth convex stochastic optimization problems (Q6053598) (← links)
• Gradient-free methods for non-smooth convex stochastic optimization with heavy-tailed noise on convex compact (Q6060544) (← links)
• Non-smooth setting of stochastic decentralized convex optimization problem over time-varying graphs (Q6060563) (← links)
• Sign stochastic gradient descents without bounded gradient assumption for the finite sum minimization (Q6072513) (← links)
• Re-thinking high-dimensional mathematical statistics. Abstracts from the workshop held May 15--21, 2022 (Q6115552) (← links)
• Online bandit convex optimisation with stochastic constraints via two-point feedback (Q6115925) (← links)
• Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization (Q6175706) (← links)
• Adaptive Catalyst for Smooth Convex Optimization (Q6329866) (← links)
• Distributed zeroth-order optimization: convergence rates that match centralized counterpart (Q6537281) (← links)
• Small errors in random zeroth-order optimization are imaginary (Q6580001) (← links)
• Privacy-preserving distributed projected one-point bandit online optimization over directed graphs (Q6583485) (← links)
• Stochastic adversarial noise in the ``black box'' optimization problem (Q6588731) (← links)
• Accelerated zero-order SGD method for solving the black box optimization problem under ``overparametrization'' condition (Q6588732) (← links)
• Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods (Q6651403) (← links)