Three Examples of Accurate Likelihood Inference

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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 ...

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 ...

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 ...

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 ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

We discuss the use of likelihood asymptotics for inference on risk measures in univariate extreme value problems, focusing on estimation of high quantiles and similar summaries of risk for uncertainty quantification. We study whether higher-order approxima ...

Genome-wide association studies rely on the statistical inference of untyped variants, called imputation, to increase the coverage of genotyping arrays. However, the results are often suboptimal in populations underrepresented in existing reference panels ...

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy ...

Long Range (LoRa) is an emerging low-power wide-area network technology offering long-range wireless connectivity to Internet of Things (IoT) devices. For energy efficiency reasons, LoRa end nodes implement a nonslotted ALOHA multiple access scheme to tran ...

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

Local modifications of a computational domain are often performed in order to simplify the meshing process and to reduce computational costs and memory requirements. However, removing geometrical features of a domain often introduces a non-negligible error ...

Three Examples of Accurate Likelihood Inference

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Boosting likelihood learning with event reweighting

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

Graph generative deep learning models with an application to circuit topologies

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

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Machine Learning Security in Industry: A Quantitative Survey

An Unbiased Method of Measuring the Ratio of Two Data Sets

Robust discrete choice models with t-distributed kernel errors

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Improved Inference On Risk Measures For Univariate Extremes

Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

A note on universal inference

Experimental data-driven model predictive control of a hospital HVAC system during regular use

A Maximum-Likelihood-Based Two-User Receiver for LoRa Chirp Spread-Spectrum Modulation

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Analysis-aware defeaturing of complex geometries