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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

Chat with 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

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

Distributional Regression and Autoregression via Optimal Transport

Universal and adaptive methods for robust stochastic optimization

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

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

Distributional Regression and Autoregression via Optimal Transport

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

Random matrix methods for high-dimensional machine learning models

Deep Learning Generalization with Limited and Noisy Labels

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

On the Generalization of Stochastic Gradient Descent with Momentum

Universal and adaptive methods for robust stochastic optimization

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

The statistical complexity of early-stopped mirror descent