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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 26–50 of 181 papers

TitleStatusHype
Adapting Newton's Method to Neural Networks through a Summary of Higher-Order DerivativesCode0
NysAct: A Scalable Preconditioned Gradient Descent using Nystrom ApproximationCode0
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic RatesCode0
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methodsCode0
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large BatchesCode0
Online Covariance Matrix Estimation in Sketched Newton MethodsCode0
A Computationally Efficient Sparsified Online Newton MethodCode0
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic ProgrammingCode0
AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional SubspacesCode0
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNsCode0
Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directionsCode0
Generalized Optimistic Methods for Convex-Concave Saddle Point ProblemsCode0
ISAAC Newton: Input-based Approximate Curvature for Newton's MethodCode0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Fast and Furious Convergence: Stochastic Second Order Methods under InterpolationCode0
FOSI: Hybrid First and Second Order OptimizationCode0
Large batch size training of neural networks with adversarial training and second-order informationCode0
Differentially Private Image Classification from FeaturesCode0
Error Feedback Can Accurately Compress PreconditionersCode0
Nonlinear matrix recovery using optimization on the Grassmann manifoldCode0
Alternating Iteratively Reweighted _1 and Subspace Newton Algorithms for Nonconvex Sparse OptimizationCode0
LIBS2ML: A Library for Scalable Second Order Machine Learning AlgorithmsCode0
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor GeneralizationCode0
Limitations of the Empirical Fisher Approximation for Natural Gradient DescentCode0
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep LearningCode0
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