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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 1–25 of 181 papers

TitleStatusHype
NysAct: A Scalable Preconditioned Gradient Descent using Nystrom ApproximationCode0
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
Accelerated Training of Federated Learning via Second-Order Methods—0
Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information AnalysisCode2
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology—0
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration—0
SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian ApproximationCode1
Online Covariance Matrix Estimation in Sketched Newton MethodsCode0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning—0
Preconditioners for the Stochastic Training of Neural Fields—0
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning—0
A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods—0
Gradient Norm Regularization Second-Order Algorithms for Solving Nonconvex-Strongly Concave Minimax Problems—0
Don't Be So Positive: Negative Step Sizes in Second-Order Methods—0
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor GeneralizationCode0
Improving Stochastic Cubic Newton with Momentum—0
Second-Order Min-Max Optimization with Lazy Hessians—0
Unlocking FedNL: Self-Contained Compute-Optimized Implementation—0
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
FLeNS: Federated Learning with Enhanced Nesterov-Newton SketchCode0
Alternating Iteratively Reweighted _1 and Subspace Newton Algorithms for Nonconvex Sparse OptimizationCode0
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm—0
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement LearningCode1
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization—0
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