The following pages link to (Q4637040):
Displaying 28 items.
- Sub-sampled Newton methods (Q1739039) (← links)
- On the local convergence of a stochastic semismooth Newton method for nonsmooth nonconvex optimization (Q2082285) (← links)
- A subsampling approach for Bayesian model selection (Q2105562) (← links)
- Stronger data poisoning attacks break data sanitization defenses (Q2127214) (← links)
- A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization (Q2149551) (← links)
- Combining stochastic adaptive cubic regularization with negative curvature for nonconvex optimization (Q2302838) (← links)
- Discriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions (Q2693789) (← links)
- (Q4558473) (← links)
- (Q4633055) (← links)
- Optimization Methods for Large-Scale Machine Learning (Q4641709) (← links)
- (Q4969259) (← links)
- (Q4998966) (← links)
- Second-Order Online Nonconvex Optimization (Q5036241) (← links)
- Sketched Newton--Raphson (Q5093644) (← links)
- An investigation of Newton-Sketch and subsampled Newton methods (Q5135249) (← links)
- Stochastic proximal quasi-Newton methods for non-convex composite optimization (Q5198046) (← links)
- Stochastic sub-sampled Newton method with variance reduction (Q5204645) (← links)
- Regularization via Mass Transportation (Q5214188) (← links)
- A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization (Q5244401) (← links)
- Automatic, dynamic, and nearly optimal learning rate specification via local quadratic approximation (Q6054924) (← links)
- Revisiting the fragility of influence functions (Q6077892) (← links)
- An overview of stochastic quasi-Newton methods for large-scale machine learning (Q6097379) (← links)
- Hessian averaging in stochastic Newton methods achieves superlinear convergence (Q6165593) (← links)
- On pseudoinverse-free block maximum residual nonlinear Kaczmarz method for solving large-scale nonlinear system of equations (Q6179938) (← links)
- Resource-adaptive Newton's method for distributed learning (Q6591485) (← links)
- A selective review on statistical methods for massive data computation: distributed computing, subsampling, and minibatch techniques (Q6620576) (← links)
- Adaptive pruning-based Newton's method for distributed learning (Q6658297) (← links)
- Trust region-type method under inexact gradient and inexact Hessian with convergence analysis (Q6665208) (← links)
