Publications associées à Estimateur (statistique)

MATHICSE Technical Report: Eigenfunction martingale estimating functions and filtered data for drift estimation of discretely observed multiscale

Assyr Abdulle, Andrea Zanoni, Grigorios A. Pavliotis

We propose a novel method for drift estimation of multiscale diffusion processes when a sequence of discrete observations is given. For the Langevin dynamics in a two-scale potential, our approach relies on the eigenvalues and the eigenfunctions of the hom ...

MATHICSE2021

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

Victor Panaretos, Anirvan Chakraborty

We develop theory and methodology for the problem of nonparametric registration of functional data that have been subjected to random deformation (warping) of their time scale. The separation of this phase variation ("horizontal" variation) from the amplit ...

2021

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to th ...

2021

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Assyr Abdulle, Giacomo Garegnani, Andrea Zanoni, Grigorios A. Pavliotis

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coeffic ...

2021

MATHICSE Technical Report : Analysis-aware defeaturing: problem setting and a posteriori estimation

Annalisa Buffa, Rafael Vazquez Hernandez, Ondine Gabrielle Chanon

Defeaturing consists in simplifying geometrical models by removing the geometrical features that are considered not relevant for a given simulation. Feature removal and simplification of computer-aided design models enables faster simulations for engineeri ...

EPFL2021

Monte Carlo Estimators for Differential Light Transport

Wenzel Alban Jakob, Tizian Lucien Zeltner, Sébastien Nicolas Speierer

Physically based differentiable rendering algorithms propagate derivatives through realistic light transport simulations and have applications in diverse areas including inverse reconstruction and machine learning. Recent progress has led to unbiased metho ...

ASSOC COMPUTING MACHINERY2021

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Fabio Nobile, Sebastian Krumscheid, Matthieu Claude Martin

We consider the numerical approximation of an optimal control problem for an elliptic Partial Differential Equation (PDE) with random coefficients. Specifically, the control function is a deterministic, distributed forcing term that minimizes the expected ...

2021

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...

2021

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Estimateur (statistique)

MATHICSE Technical Report: Eigenfunction martingale estimating functions and filtered data for drift estimation of discretely observed multiscale

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

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Sparsely Observed Functional Time Series: Theory and Applications

MATHICSE Technical Report : Analysis-aware defeaturing: problem setting and a posteriori estimation

Monte Carlo Estimators for Differential Light Transport

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Learning Self-Exciting Temporal Point Processes Under Noisy Observations

Graph Chatbot

Chattez avec Graph Search

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

MATHICSE Technical Report: Eigenfunction martingale estimating functions and filtered data for drift estimation of discretely observed multiscale

Sparsely Observed Functional Time Series: Theory and Applications

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

Drift Estimation of Multiscale Diffusions Based on Filtered Data

MATHICSE Technical Report : Analysis-aware defeaturing: problem setting and a posteriori estimation

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Learning Self-Exciting Temporal Point Processes Under Noisy Observations

Monte Carlo Estimators for Differential Light Transport

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters