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Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

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Distributional Regression and Autoregression via Optimal Transport

Laya Ghodrati

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

EPFL2023

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Graph Chatbot

Chattez avec Graph Search

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Random matrix methods for high-dimensional machine learning models

On the Generalization of Stochastic Gradient Descent with Momentum

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Distributional Regression and Autoregression via Optimal Transport

Universal and adaptive methods for robust stochastic optimization

Deep Learning Generalization with Limited and Noisy Labels

The statistical complexity of early-stopped mirror descent

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

Deep Learning Generalization with Limited and Noisy Labels

Random matrix methods for high-dimensional machine learning models

Distributional Regression and Autoregression via Optimal Transport

Universal and adaptive methods for robust stochastic optimization

On the Generalization of Stochastic Gradient Descent with Momentum

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

The statistical complexity of early-stopped mirror descent