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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 51–100 of 181 papers

TitleStatusHype
A Computationally Efficient Sparsified Online Newton MethodCode0
AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional SubspacesCode0
Studying K-FAC Heuristics by Viewing Adam through a Second-Order LensCode0
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function—0
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold—0
Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods—0
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Error Feedback Can Accurately Compress PreconditionersCode0
Sharpened Lazy Incremental Quasi-Newton MethodCode0
Minibatching Offers Improved Generalization Performance for Second Order Optimizers—0
Faster Differentially Private Convex Optimization via Second-Order Methods—0
ISAAC Newton: Input-based Approximate Curvature for Newton's MethodCode0
Adaptive Consensus Optimization Method for GANsCode0
Gradient-Boosted Based Structured and Unstructured Learning—0
FOSI: Hybrid First and Second Order OptimizationCode0
Stochastic Dimension-reduced Second-order Methods for Policy Optimization—0
On backpropagating Hessians through ODEs—0
A survey of deep learning optimizers -- first and second order methods—0
Differentially Private Image Classification from FeaturesCode0
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and SparsityCode0
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods—0
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee—0
Component-Wise Natural Gradient Descent -- An Efficient Neural Network Optimization—0
Mirror Prox Algorithm for Large-Scale Cell-Free Massive MIMO Uplink Power Control—0
Adaptive Second Order Coresets for Data-efficient Machine Learning—0
SP2: A Second Order Stochastic Polyak Method—0
A Newton-CG based barrier method for finding a second-order stationary point of nonconvex conic optimization with complexity guarantees—0
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large BatchesCode0
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic ProgrammingCode0
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function—0
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning—0
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization—0
Accelerating Stochastic Probabilistic Inference—0
Amortized Proximal Optimization—0
Generalized Optimistic Methods for Convex-Concave Saddle Point ProblemsCode0
A Mini-Block Fisher Method for Deep Neural Networks—0
Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink—0
SCORE: Approximating Curvature Information under Self-Concordant Regularization—0
Newton methods based convolution neural networks using parallel processing—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Provable Regret Bounds for Deep Online Learning and Control—0
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey—0
SLIM-QN: A Stochastic, Light, Momentumized Quasi-Newton Optimizer for Deep Neural Networks—0
Nonlinear matrix recovery using optimization on the Grassmann manifoldCode0
Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information—0
Structured second-order methods via natural gradient descent—0
Bilinear Parameterization for Non-Separable Singular Value Penalties—0
LocoProp: Enhancing BackProp via Local Loss OptimizationCode0
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