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Online Robust Regression via SGD on the l1 loss

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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

Online Robust Regression via SGD on the l1 loss

Graph Chatbot

Chat with Graph Search

Understanding generalization and robustness in modern deep learning

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

Distributional Regression and Autoregression via Optimal Transport

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Sparsest piecewise-linear regression of one-dimensional data

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

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Bayesian regression explains how human participants handle parameter uncertainty

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

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

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Understanding generalization and robustness in modern deep learning

Distributional Regression and Autoregression via Optimal Transport

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Bayesian regression explains how human participants handle parameter uncertainty

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

Sparsest piecewise-linear regression of one-dimensional data

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing