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Functional registration and local variations: Identifiability, rank, and tuning

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Discrete stochastic heat equation driven by fractional noise

Kamran Kalbasi

We consider the parabolic Anderson model on

\mathbb{Z}^d

driven by fractional noise. We prove that it has a mild solution given by Feynman-Kac representation which coincides with the partition function of a directed polymer in a fractional Brownian envir ...

EPFL2014

Functional registration and local variations: Identifiability, rank, and tuning

Graph Chatbot

Chattez avec Graph Search

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

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

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Residual-based a posteriori error estimation for contact problems approximated by Nitsche's method

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Method to determine the distribution of temperature sensors

Discrete stochastic heat equation driven by fractional noise

MAP Estimators for Self-Similar Sparse Stochastic Models

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Residual-based a posteriori error estimation for contact problems approximated by Nitsche's method

MAP Estimators for Self-Similar Sparse Stochastic Models

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

Discrete stochastic heat equation driven by fractional noise

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Method to determine the distribution of temperature sensors

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers