Contributions to Likelihood-Based Modelling of Extreme Values

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Analyzing neutron noise in zero-power reactors provides information about integral parameters, such as the prompt decay constant

\alpha

alpha. This information can be used to validate codes that predict said parameters, or time-dependent behavior, part ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form "empirical-Bernstein" CSs and ...

The thesis explores the issue of fairness in the real-time (RT) control of battery energy storage systems (BESSs) hosted in active distribution networks (ADNs) in the presence of uncertainties by proposing and experimentally validating appropriate control ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

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 ...

As a follow-up to the CEA-EPFL PETALE experimental program on stainless steel nuclear data, the EPFL initiated an open-loop pile-oscillation experimental program in the CROCUS reactor: BLOOM. A reproduction of the critical experiments of PETALE, the progra ...

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 ...

Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

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 ...

High-level waste, stemming from nuclear electricity generation poses significant environmental and safety concerns. Currently, high-level wastes are stored in interim facilities needing constant monitoring and waiting for a definitive solution. Deep geolog ...

The p-Laplacian problem -del & sdot; ((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher ord ...

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

The probability of detecting technosignatures (i.e., evidence of technological activity beyond Earth) increases with their longevity, or the time interval over which they manifest. Therefore, the assumed distribution of longevities has some bearing on the ...

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 ...

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 ...

Cells are the smallest operational units of living systems. Through synthesis of various biomolecules and exchange of signals with the environment, cells tightly regulate their composition to realize a specific functional state. The transformation of a cel ...

Contributions to Likelihood-Based Modelling of Extreme Values

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Measurement of the Prompt Neutron Decay Constant in a Zero-Power Reactor Using a Novel 3D Detector System

Boosting likelihood learning with event reweighting

Extensions of Peer Prediction Incentive Mechanisms

Spectral Estimators for High-Dimensional Matrix Inference

Anthony C Davison and Igor Rodionov's contribution to the Discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas

Fair real-time control of energy storage systems in active distribution networks in the presence of uncertainties

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

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

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

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

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Graph generative deep learning models with an application to circuit topologies

A FEM modelling workflow to simulate THM effects in the rock around nuclear waste packages

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

Exploiting the Signal-Leak Bias in Diffusion Models

Technosignatures Longevity and Lindy's Law

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

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

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