Pages that link to "Item:Q5405257"

The following pages link to Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization (Q5405257):

Displaying 50 items.

• On data preconditioning for regularized loss minimization (Q285940) (← links)
• Inexact coordinate descent: complexity and preconditioning (Q306308) (← links)
• On optimal probabilities in stochastic coordinate descent methods (Q315487) (← links)
• Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization (Q486721) (← links)
• A novel Frank-Wolfe algorithm. Analysis and applications to large-scale SVM training (Q508681) (← links)
• Minimizing finite sums with the stochastic average gradient (Q517295) (← links)
• Distributed block-diagonal approximation methods for regularized empirical risk minimization (Q782443) (← links)
• High-dimensional model recovery from random sketched data by exploring intrinsic sparsity (Q782446) (← links)
• Convergence properties of a randomized primal-dual algorithm with applications to parallel MRI (Q826182) (← links)
• A flexible coordinate descent method (Q1639710) (← links)
• The complexity of primal-dual fixed point methods for ridge regression (Q1669015) (← links)
• Linear convergence rate for the MDM algorithm for the nearest point problem (Q1678696) (← links)
• Inexact proximal stochastic gradient method for convex composite optimization (Q1694394) (← links)
• Dual block-coordinate forward-backward algorithm with application to deconvolution and deinterlacing of video sequences (Q1704003) (← links)
• Extended ADMM and BCD for nonseparable convex minimization models with quadratic coupling terms: convergence analysis and insights (Q1717220) (← links)
• An optimal randomized incremental gradient method (Q1785198) (← links)
• Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training (Q1790674) (← links)
• Stochastic gradient method with Barzilai-Borwein step for unconstrained nonlinear optimization (Q1995392) (← links)
• Convergence of stochastic proximal gradient algorithm (Q2019902) (← links)
• Point process estimation with Mirror Prox algorithms (Q2019904) (← links)
• Generalized stochastic Frank-Wolfe algorithm with stochastic ``substitute'' gradient for structured convex optimization (Q2020608) (← links)
• Analysis of biased stochastic gradient descent using sequential semidefinite programs (Q2020610) (← links)
• Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods (Q2023684) (← links)
• Randomized smoothing variance reduction method for large-scale non-smooth convex optimization (Q2033403) (← links)
• Stochastic quasi-gradient methods: variance reduction via Jacobian sketching (Q2039235) (← links)
• A stochastic subspace approach to gradient-free optimization in high dimensions (Q2044475) (← links)
• Fastest rates for stochastic mirror descent methods (Q2044496) (← links)
• Inverse optimization approach to the identification of electricity consumer models (Q2045617) (← links)
• Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression (Q2057761) (← links)
• A hybrid stochastic optimization framework for composite nonconvex optimization (Q2118109) (← links)
• Accelerating mini-batch SARAH by step size rules (Q2127094) (← links)
• Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems (Q2133414) (← links)
• A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization (Q2149551) (← links)
• Finite-sum smooth optimization with SARAH (Q2149950) (← links)
• Communication-efficient distributed multi-task learning with matrix sparsity regularization (Q2183594) (← links)
• Linear convergence of cyclic SAGA (Q2193004) (← links)
• Principal component projection with low-degree polynomials (Q2219638) (← links)
• Worst-case complexity of cyclic coordinate descent: \(O(n^2)\) gap with randomized version (Q2220668) (← links)
• Near-optimal discrete optimization for experimental design: a regret minimization approach (Q2227544) (← links)
• Forward-reflected-backward method with variance reduction (Q2231039) (← links)
• Markov chain block coordinate descent (Q2301127) (← links)
• Provable accelerated gradient method for nonconvex low rank optimization (Q2303662) (← links)
• Efficient learning with robust gradient descent (Q2320583) (← links)
• Dual coordinate ascent methods for non-strictly convex minimization (Q2368079) (← links)
• Proximal average approximated incremental gradient descent for composite penalty regularized empirical risk minimization (Q2398094) (← links)
• Generalized forward-backward splitting with penalization for monotone inclusion problems (Q2423787) (← links)
• An accelerated variance reducing stochastic method with Douglas-Rachford splitting (Q2425236) (← links)
• Negotiating multicollinearity with spike-and-slab priors (Q2513696) (← links)
• Concentration inequalities for sampling without replacement (Q2515502) (← links)
• Improving kernel online learning with a snapshot memory (Q2673322) (← links)