Publications related to Model selection

Spectral Estimators for High-Dimensional Matrix Inference

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 ...

EPFL2024

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Andreas Fuster

We conduct two survey experiments to study which information people choose to consume and how it affects their beliefs. In the first experiment, respondents choose between optimistic and pessimistic article headlines related to the COVID-19 pandemic and ar ...

2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Anomalous Dissipation and Lack of Selection in the Obukhov-Corrsin Theory of Scalar Turbulence

Maria Colombo, Massimo Sorella

The Obukhov-Corrsin theory of scalar turbulence [21, 54] advances quantitative predictions on passive-scalar advection in a turbulent regime and can be regarded as the analogue for passive scalars of Kolmogorov's K41 theory of fully developed turbulence [4 ...

Springernature2023

Self-Interaction and Polarons in Density Functional Theory

Stefano Falletta

The electron self-interaction is a long-standing problem in density functional theory and is particularly critical in the description of polarons. Polarons are quasiparticles involving charge localization coupled with self-induced lattice distortions. Sinc ...

EPFL2023

Bed Topography Inference from Velocity Field Using Deep Learning

Christophe Ancey, Mehrdad Kiani Oshtorjani

Measuring bathymetry has always been a major scientific and technological challenge. In this work, we used a deep learning technique for inferring bathymetry from the depth-averaged velocity field. The training of the neural network is based on 5742 labora ...

2023

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Soumaya Elkantassi

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. ...

EPFL2023

Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results

Gabriele Manoli, Matthias Roth

Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban-focussed la ...

Wiley2023

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Adrien Michel, Tobias Jonas

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model a ...

COPERNICUS GESELLSCHAFT MBH2023

Robust discrete choice models with t-distributed kernel errors

Michel Bierlaire, Thomas Gasos, Prateek Bansal

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, ...

2023

A benchmark dataset for machine learning in ecotoxicology

Kristin Schirmer, Marco Baity-Jesi, Fernando Perez Cruz

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, ...

Nature Portfolio2023

Non-Gaussian superradiant transition via three-body ultrastrong coupling

Fabrizio Minganti

We introduce a class of quantum optical Hamiltonians characterized by three-body couplings and propose a circuit-QED scheme based on state-of-the-art technology that implements the considered model. Unlike two-body light-matter interactions, this three-bod ...

AMER PHYSICAL SOC2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

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 ...

EPFL2023

SporTran: A code to estimate transport coefficients from the cepstral analysis of (multivariate) current time series

Loris Ercole

SporTran is a Python utility designed to estimate generic transport coefficients in extended systems, based on the Green-Kubo theory of linear response and the recently introduced cepstral analysis of the current time series generated by molecular dynamics ...

ELSEVIER2022

Hubbard U through polaronic defect states

Alfredo Pasquarello, Stefano Falletta

Since the preliminary work of Anisimov and co-workers, the Hubbard corrected DFT+U functional has been used for predicting properties of correlated materials by applying on-site effective Coulomb interactions to specific orbitals. However, the determinatio ...

NATURE PORTFOLIO2022

Space Oddity? A Statistical Formulation of Conjunction Assessment

Anthony Christopher Davison, Soumaya Elkantassi

Satellite conjunctions involving "near misses" of space objects are becoming increasingly likely. One approach to risk analysis for them involves the computation of the collision probability, but this has been regarded as having some counterintuitive prope ...

AMER INST AERONAUTICS ASTRONAUTICS2022

ExG Signal Feature Selection Using Hyperdimensional Computing Encoding

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Wearable IoT devices and novel continuous monitoring algorithms are essential components of the healthcare transition from reactive interventions focused on symptom treatment to more proactive prevention, from one-size-fits-all to personalized medicine, an ...

IEEE2022

Thermal Modelling and Experimental Validation in the Perspective of Tool Steel Laser Polishing

Eric Boillat, Alexandre Masserey, Bastian Meylan

Laser polishing (LP) is a process that allows a significant reduction of the surface roughness of a metal workpiece via re-melting a shallow layer of material. However, the practical use of LP is limited due to the difficulty of satisfying the high surface ...

MDPI2022

Explicit Stabilized Multirate Method For Stiff Stochastic Differential Equations

Assyr Abdulle, Giacomo Rosilho De Souza

Stabilized explicit methods are particularly efficient, for large systems of stiff stochastic differential equations (SDEs) due to their extended stability domain. However, they lose their efficiency when a severe stiffness is induced by very few "fast" de ...

SIAM PUBLICATIONS2022

Fragment ion-functional group relationships in organic aerosols using aerosol mass spectrometry and mid-infrared spectroscopy

Satoshi Takahama, Amir Yazdani, Imad El Haddad, Nikunj Dudani

Aerosol mass spectrometry (AMS) and mid-infrared spectroscopy (MIR) are two analytical methods for characterizing the chemical composition of OM. While AMS provides high-temporal-resolution bulk measurements, the extensive fragmentation during the electron ...

2022