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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

Rate of convergence

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Enabling Uncertainty Estimation in Iterative Neural Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Randomized flexible GMRES with deflated restarting

Residual-based attention in physics-informed neural networks

Quantitative convergence rates for scaling limit of SPDEs with transport noise

Stable Nonconvex-Nonconcave Training via Linear Interpolation

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Stochastic adaptive resampling for the estimation of discrete choice models

Distributional Regression and Autoregression via Optimal Transport

Homogenization results for the generator of multiscale Langevin dynamics in weighted Sobolev spaces

Quantitative convergence rates for scaling limit of SPDEs with transport noise

Randomized flexible GMRES with deflated restarting

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Enabling Uncertainty Estimation in Iterative Neural Networks

Residual-based attention in physics-informed neural networks

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Distributional Regression and Autoregression via Optimal Transport

Stochastic adaptive resampling for the estimation of discrete choice models

Homogenization results for the generator of multiscale Langevin dynamics in weighted Sobolev spaces

Stable Nonconvex-Nonconcave Training via Linear Interpolation