Publications related to Estimation of covariance matrices

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

Mean-Covariance Robust Risk Measurement

Daniel Kuhn, Damir Filipovic, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh

We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization. We model uncertainty in terms of the Gelbrich distance on the mean-covariance space, along with prior structural information about the population dis ...

2021

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Jean-Philippe Thiran, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Marco Pizzolato, Gian Franco Piredda, Thomas Yu, Alessandro Daducci, Nicolas Kunz

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is ...

2021

Characterization of Microwave Substrates for High Accuracy and Long-Term Stability Using Full-Wave Microstrip Ring Resonator Method

Anja Skrivervik, Yuanyan Su, Miroslav Veljovic

An accurate material characterization method for microwave printed-circuit-board substrates is proposed by combining the microwave ring resonator method with iterative full-wave simulations. A detailed error analysis is performed quantitatively to compensa ...

IEEE2021

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Patrick Thiran, Farnood Salehi, Mahsa Forouzesh

Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand their generalization properties. We shed light on this matter by linking the lo ...

IEEE2021

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Daniel Kuhn, Viet Anh Nguyen, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...

2020

Assessment of representativity of the PETALE experiments for validation of Swiss LWRs ex-core dosimetry calculations

Andreas Pautz, Vincent Pierre Lamirand, Hakim Ferroukhi, Dimitri Rochman, Axel Guy Marie Laureau, Alexander Vasiliev

The international experimental program PETALE will be carried out at the CROCUS research reactor of EPFL. The program aims at measuring neutron penetration in slabs made of materials composing typical LWR reactor pressure vessel. The measurements will be u ...

E D P SCIENCES2020

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Ali H. Sayed, Roula Nassif

In this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular grap ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Certified And Fast Computations With Shallow Covariance Kernels

Daniel Kressner, Stefano Massei

Many techniques for data science and uncertainty quantification demand efficient tools to handle Gaussian random fields, which are defined in terms of their mean functions and covariance operators. Recently, parameterized Gaussian random fields have gained ...

AMER INST MATHEMATICAL SCIENCES-AIMS2020

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Estimation of covariance matrices

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Mean-Covariance Robust Risk Measurement

Compact atomic descriptors enable accurate predictions via linear models

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Characterization of Microwave Substrates for High Accuracy and Long-Term Stability Using Full-Wave Microstrip Ring Resonator Method

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Assessment of representativity of the PETALE experiments for validation of Swiss LWRs ex-core dosimetry calculations

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Certified And Fast Computations With Shallow Covariance Kernels

Graph Chatbot

Chat with Graph Search

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Assessment of representativity of the PETALE experiments for validation of Swiss LWRs ex-core dosimetry calculations

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Compact atomic descriptors enable accurate predictions via linear models

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Certified And Fast Computations With Shallow Covariance Kernels

Characterization of Microwave Substrates for High Accuracy and Long-Term Stability Using Full-Wave Microstrip Ring Resonator Method

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Mean-Covariance Robust Risk Measurement

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models