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Functional lagged regression with sparse noisy observations

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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

Functional lagged regression with sparse noisy observations

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Chat with Graph Search

On distributional autoregression and iterated transportation

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

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

Distributional Regression and Autoregression via Optimal Transport

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Roles of Clinical Features and Chest CT in Predicting the Outcomes of Hospitalized Patients with COVID-19 Developing AKI

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Sparsely Observed Functional Time Series: Theory and Applications

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

On distributional autoregression and iterated transportation

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

Roles of Clinical Features and Chest CT in Predicting the Outcomes of Hospitalized Patients with COVID-19 Developing AKI

Distributional Regression and Autoregression via Optimal Transport

Sparsely Observed Functional Time Series: Theory and Applications

Daily Contributors of Tinnitus Loudness and Distress: An Ecological Momentary Assessment Study

On the rate of convergence for the autocorrelation operator in functional autoregression

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Distribution-on-distribution regression via optimal transport maps