Publications associées à Faster Coordinate Descent via Adaptive Importance Sampling

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Faster Coordinate Descent via Adaptive Importance Sampling

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On the Generalization of Stochastic Gradient Descent with Momentum

Scalable constrained optimization

Topics in statistical physics of high-dimensional machine learning

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

The complexity of quantum support vector machines

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization

Scalable constrained optimization

On the Generalization of Stochastic Gradient Descent with Momentum

The complexity of quantum support vector machines

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Topics in statistical physics of high-dimensional machine learning

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization