Publications associées à Global optimization

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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

Global optimization

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HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

Bridging the gap between model-driven and data-driven methods in the era of Big Data

Proximal Point Imitation Learning

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

Overcoming decision paralysis—A digital twin for decision making in energy system design

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

A universal workflow for creation, validation and generalization of detailed neuronal models

Material removing tool and method for designing the same

Sharp asymptotics on the compression of two-layer neural networks

Frequency-domain data-driven position-dependent controller synthesis for Cartesian Robots

Overcoming decision paralysis—A digital twin for decision making in energy system design

Frequency-domain data-driven position-dependent controller synthesis for Cartesian Robots

A Noise-Resistant Mixed-Discrete Particle Swarm Optimization Algorithm for the Automatic Design of Robotic Controllers

Material removing tool and method for designing the same

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

Sharp asymptotics on the compression of two-layer neural networks

A universal workflow for creation, validation and generalization of detailed neuronal models

HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

Proximal Point Imitation Learning

Bridging the gap between model-driven and data-driven methods in the era of Big Data