Publications associées à Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

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

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Proximal Point Imitation Learning

Geometrical Treatise on the Modelling of 3D Particulate Inclusion-Matrix Microstructures with an Application to Historical Stone Masonry Walls

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

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

High-dimensional optimization under nonconvex excluded volume constraints

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

High-dimensional optimization under nonconvex excluded volume constraints

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

Geometrical Treatise on the Modelling of 3D Particulate Inclusion-Matrix Microstructures with an Application to Historical Stone Masonry Walls

Proximal Point Imitation Learning

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

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

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Extra Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods