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Consistent Sobolev Regression via Fuzzy Systems with Overlapping Concepts

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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

Consistent Sobolev Regression via Fuzzy Systems with Overlapping Concepts

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Chat with Graph Search

Distributional Regression and Autoregression via Optimal Transport

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

Minimax rate for optimal transport regression between distributions

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

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Bayesian regression explains how human participants handle parameter uncertainty

Avoidance of Convex and Concave Obstacles with Convergence ensured through Contraction

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

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

Data-driven reduced order modeling for time-dependent problems

Distributional Regression and Autoregression via Optimal Transport

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

Minimax rate for optimal transport regression between distributions

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Bayesian regression explains how human participants handle parameter uncertainty

Avoidance of Convex and Concave Obstacles with Convergence ensured through Contraction

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Data-driven reduced order modeling for time-dependent problems

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

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