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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

Distribution-on-distribution regression via optimal transport maps

Bayesian regression explains how human participants handle parameter uncertainty

Bayesian modeling of brain function and behavior

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

Adaptive isogeometric analysis on two-dimensional trimmed domains based on a hierarchical approach

MATHICSE Technical Report: Adaptive isogeometric analysis on two-dimensional trimmed domains based on a hierarchical approach

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Distributional Regression and Autoregression via Optimal Transport

Minimax rate for optimal transport regression between distributions

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

Adaptive isogeometric analysis on two-dimensional trimmed domains based on a hierarchical approach

Bayesian regression explains how human participants handle parameter uncertainty

MATHICSE Technical Report: Adaptive isogeometric analysis on two-dimensional trimmed domains based on a hierarchical approach

Distribution-on-distribution regression via optimal transport maps

Bayesian modeling of brain function and behavior

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

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