Pages that link to "Item:Q715253"

The following pages link to Sample size selection in optimization methods for machine learning (Q715253):

Displaying 33 items.

• Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization (Q5144794) (← links)
• Convergence of Newton-MR under Inexact Hessian Information (Q5148404) (← links)
• Quantity optimization of virtual sample generation with two kinds of upper bound conditions (Q5194148) (← links)
• A Stochastic Line Search Method with Expected Complexity Analysis (Q5215517) (← links)
• Solving inverse problems using data-driven models (Q5230520) (← links)
• An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration (Q5231671) (← links)
• A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization (Q5244401) (← links)
• Newton-like Method with Diagonal Correction for Distributed Optimization (Q5275293) (← links)
• Extragradient Method with Variance Reduction for Stochastic Variational Inequalities (Q5737725) (← links)
• LSOS: Line-search second-order stochastic optimization methods for nonconvex finite sums (Q5879118) (← links)
• Probability maximization via Minkowski functionals: convex representations and tractable resolution (Q6038654) (← links)
• An adaptive stochastic sequential quadratic programming with differentiable exact augmented Lagrangians (Q6038658) (← links)
• Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming (Q6052061) (← links)
• A trust region method for noisy unconstrained optimization (Q6052069) (← links)
• An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints (Q6072951) (← links)
• Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method (Q6087955) (← links)
• An overview of stochastic quasi-Newton methods for large-scale machine learning (Q6097379) (← links)
• A framework of convergence analysis of mini-batch stochastic projected gradient methods (Q6097385) (← links)
• Adaptive sampling stochastic multigradient algorithm for stochastic multiobjective optimization (Q6142067) (← links)
• A line search based proximal stochastic gradient algorithm with dynamical variance reduction (Q6159404) (← links)
• Hessian averaging in stochastic Newton methods achieves superlinear convergence (Q6165593) (← links)
• Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization (Q6175706) (← links)
• A greedy average block sparse Kaczmarz method for sparse solutions of linear systems (Q6549107) (← links)
• A stochastic variance reduced gradient method with adaptive step for stochastic optimization (Q6565722) (← links)
• A stochastic gradient method with variance control and variable learning rate for deep learning (Q6582036) (← links)
• Estimating absorption and scattering in quantitative photoacoustic tomography with an adaptive Monte Carlo method for light transport (Q6593064) (← links)
• The sparse Kaczmarz method with surrogate hyperplane for the regularized basis pursuit problem (Q6593333) (← links)
• Subsampled first-order optimization methods with applications in imaging (Q6606441) (← links)
• First- and second-order high probability complexity bounds for trust-region methods with noisy oracles (Q6608030) (← links)
• Bolstering stochastic gradient descent with model building (Q6635852) (← links)
• An investigation of stochastic trust-region based algorithms for finite-sum minimization (Q6644989) (← links)
• A multilevel method for self-concordant minimization (Q6655798) (← links)
• A proximal stochastic quasi-Newton algorithm with dynamical sampling and stochastic line search (Q6655883) (← links)