Bivariate Extreme Statistics, Ii

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Conditional cumulative distribution functions (CDFs), conditional probability density functions (PDFs), and derivatives thereof, are important parameters of interest in statistics, econometrics, and other data science disciplines. The package lpcde impleme ...

"Soft"muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously su ...

Recent advances in this understanding of light-matter interactions, combined with innovations in the design and fabrication of large-scale nanostructured metasurfaces, have enabled transformative approaches to biosensing and bioimaging. This review delves ...

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 ...

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 ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

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 ...

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligen ...

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

We introduce the elliptical Ornstein-Uhlenbeck (OU) process, which is a generalisation of the well-known univariate OU process to bivariate time series. This process maps out elliptical stochastic oscillations over time in the complex plane, which are obse ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

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. ...

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

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 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 ...

We consider the problem of nonparametric estimation of the drift and diffusion coefficients of a Stochastic Differential Equation (SDE), based on n independent replicates {Xi(t) : t is an element of [0 , 1]}13 d B(t), where alpha is an element of {0 , 1} a ...

Bivariate Extreme Statistics, Ii

Chattez avec Graph Search

lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV

Optical Metasurfaces for the Next-Generation Biosensing and Bioimaging

Spectral Estimators for High-Dimensional Matrix Inference

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

Graph generative deep learning models with an application to circuit topologies

Random matrix methods for high-dimensional machine learning models

OASIS: An integrated optimisation framework for activity scheduling

Extensions of Peer Prediction Incentive Mechanisms

Real-Time Localization for Closed-Loop Control of Assistive Furniture

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

The elliptical Ornstein-Uhlenbeck process

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Robust Outlier Rejection for 3D Registration with Variational Bayes

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Towards a multiscale point cloud structural similarity metric

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Robust discrete choice models with t-distributed kernel errors

An Unbiased Method of Measuring the Ratio of Two Data Sets

Nonparametric estimation for SDE with sparsely sampled paths: An FDA perspective