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

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Multi-frequency and Doppler spectral radars are valuable tools for retrieving information on the microphysics of precipitation, particularly snowfall. However, estimating microphysical properties from radar measurements is an ill-posed problem, i.e., diffe ...

Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and is often unknown. Consequently, recen ...

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, t ...

This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged, market-neutral portfolios. We forecast 1-year-ahead market betas using various estimating methods, including ordinary lea ...

We study the problem of learning unknown parameters in stochastic interacting particle systems with polynomial drift, interaction, and diffusion functions from the path of one single particle in the system. Our estimator is obtained by solving a linear sys ...

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 ...

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 ...

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 ...

In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...

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 ...

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 ...

Thin-laminate composites with thicknesses below 200 mu m hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusi ...

A user’s benefit from the energy stored in a battery over its lifetime depends on the time-varying characteristics of the battery, which are in turn affected by the chosen usage behavior. Both the capacity shrinkage and the number of lifetime cycles are st ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

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 ...

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 ...

The ITER Electron Cyclotron Resonance Heating and Current Drive (ECRH&CD) system relies on 1 MW 170 GHz Gyrotrons to provide the mm-wave power needed for plasma heating, current drive, and magneto-hydrodynamic control. The design and modeling of the contro ...

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 ...

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

Chattez avec Graph Search

What can radars tell us about snowfall microphysics? Insights from a Markov chain Monte-Carlo approach

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

Improved bounds and inference on optimal regimes

eLife Assessment: A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

Crypto market betas: the limits of predictability and hedging

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

Extensions of Peer Prediction Incentive Mechanisms

Random matrix methods for high-dimensional machine learning models

Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons

Stability: a search for structure

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

Graph generative deep learning models with an application to circuit topologies

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Optimal Depth of Discharge for Electric Batteries with Robust Capacity-Shrinkage Estimator

On distributional autoregression and iterated transportation

Reliable data-driven decision-making through optimal transport

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

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

Sensitivity Analysis of the Lumped Thermal Model of the EU 170 GHz Gyrotron Magnetron Injection Gun

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