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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
When Does Preconditioning Help or Hurt Generalization?—0
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
A block coordinate descent optimizer for classification problems exploiting convexity—0
Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink—0
Accelerated Training of Federated Learning via Second-Order Methods—0
Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix—0
Accelerating Stochastic Probabilistic Inference—0
A Chaos Theory Approach to Understand Neural Network Optimization—0
A comparison of second-order methods for deep convolutional neural networks—0
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization—0
A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods—0
Adaptive Optimization Algorithms for Machine Learning—0
Adaptive Second Order Coresets for Data-efficient Machine Learning—0
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time—0
A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization—0
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training—0
A Distributed Second-Order Algorithm You Can Trust—0
Preconditioners for the Stochastic Training of Neural Fields—0
Provable Regret Bounds for Deep Online Learning and Control—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Quasi-Newton Quasi-Monte Carlo for variational Bayes—0
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology—0
Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training—0
RES: Regularized Stochastic BFGS Algorithm—0
Saddle-free Hessian-free Optimization—0
SCORE: Approximating Curvature Information under Self-Concordant Regularization—0
Second Order Bilinear Discriminant Analysis for single trial EEG analysis—0
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations—0
Second-Order Kernel Online Convex Optimization with Adaptive Sketching—0
Second Order Methods for Bandit Optimization and Control—0
Second-Order Min-Max Optimization with Lazy Hessians—0
Second-order Neural Network Training Using Complex-step Directional Derivative—0
Second-Order Optimization for Non-Convex Machine Learning: An Empirical Study—0
SLIM-QN: A Stochastic, Light, Momentumized Quasi-Newton Optimizer for Deep Neural Networks—0
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm—0
SP2: A Second Order Stochastic Polyak Method—0
Stochastic Dimension-reduced Second-order Methods for Policy Optimization—0
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent—0
Stochastic Heavy Ball—0
Stochastic Newton Proximal Extragradient Method—0
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function—0
Stochastic Quasi-Newton Langevin Monte Carlo—0
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient—0
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function—0
Stochastic Subspace Cubic Newton Method—0
Structured second-order methods via natural gradient descent—0
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods—0
Sub-Sampled Newton Methods II: Local Convergence Rates—0
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