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Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Calcul infinitésimal

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ALMOST SURE SCATTERING OF THE ENERGY-CRITICAL NLS IN d > 6

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

Stochastic derivative estimation for max-stable random fields

One- and two-step semi-Lagrangian integrators for arbitrary Lagrangian-Eulerian-finite element two-phase flow simulations

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Spectral analysis for transmission eigenvalue problems with and without the complementing conditions

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Differentiable Signed Distance Function Rendering

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Stochastic derivative estimation for max-stable random fields

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Spectral analysis for transmission eigenvalue problems with and without the complementing conditions

Differentiable Signed Distance Function Rendering

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

ALMOST SURE SCATTERING OF THE ENERGY-CRITICAL NLS IN d > 6

One- and two-step semi-Lagrangian integrators for arbitrary Lagrangian-Eulerian-finite element two-phase flow simulations

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization