Publications associées à Is there an analog of Nesterov acceleration for gradient-based MCMC?

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Graph Chatbot

Chattez avec Graph Search

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Least-Squares Padé approximation of parametric and stochastic Helmholtz maps

Fast Mean Estimation with Sub-Gaussian Rates

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Safe Adaptive Importance Sampling

Sampling at unknown locations, with an application in surface retrieval

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

Fast Mean Estimation with Sub-Gaussian Rates

Least-Squares Padé approximation of parametric and stochastic Helmholtz maps

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Mirrored Langevin Dynamics

Mirrored Langevin Dynamics

Sampling at unknown locations, with an application in surface retrieval

Safe Adaptive Importance Sampling

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension