Publication

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

Related publications (50)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

Graph Chatbot

Chat with Graph Search

A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Stability: a search for structure

Spectral Estimators for High-Dimensional Matrix Inference

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

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

Parametric Estimation For Functional Autoregressive Processes On The Sphere

Functional estimation of anisotropic covariance and autocovariance operators on the sphere

A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Stability: a search for structure

Spectral Estimators for High-Dimensional Matrix Inference

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

Parametric Estimation For Functional Autoregressive Processes On The Sphere

Functional estimation of anisotropic covariance and autocovariance operators on the sphere

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