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Drift Estimation of Multiscale Diffusions Based on Filtered Data

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Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

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Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

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Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...

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TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

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2024

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

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Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

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In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

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High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

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This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

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Deciphering the nature of cell state transitions in single cells using quantitative modeling of temporal dynamics

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Design of an Open-Loop Pile-Oscillation Program in the CROCUS Reactor

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Graph generative deep learning models with an application to circuit topologies

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Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

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Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

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For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

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Influence of model uncertainty and long term deformations in action effects calculation in reinforced concrete structures

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Most codes of practice adopt a semi probabilistic design approach for the dimensioning and assessment of structures. Accordingly, structural safety is ensured by performing limit state verifications using design values determined with adequately calibrated ...

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An equilibrated flux a posteriori error estimator for defeaturing problems

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An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

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Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Aurelio Muttoni, Alain Nussbaumer, Xhemsi Malja

Ernst & Sohn2024

Influence of model uncertainty and long term deformations in action effects calculation in reinforced concrete structures

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An equilibrated flux a posteriori error estimator for defeaturing problems

Annalisa Buffa, Rafael Vazquez Hernandez, Denise Grappein, Ondine Gabrielle Chanon

2023