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Related publications (30)

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On double-descent in uncertainty quantification in overparametrized models

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Lucas Andry Clarte

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

PMLR Proceedings of Machine Learning Research2023

Climatic and Economic Background Determine the Disparities in Urbanites’ Expressed Happiness during the Summer Heat

Gabriele Manoli

Climate-change-induced extreme weather events increase heat-related mortality and health risks for urbanites, which may also affect urbanites’ expressed happiness (EH) and well-being. However, the links among EH, climate, and socioeconomic factors remain u ...

2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Olga Fink, Luca Biggio

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...

2023

Calibration Transfer Methodology for Cloud Radars Based on Ice Cloud Observations

Alexis Berne, Anne-Claire Marie Billault--Roux

This article presents a calibration transfer methodology that can be used between radars of the same or dif-ferent frequency bands. This method enables the absolute calibration of a cloud radar by transferring it from another collocated instrument with kno ...

AMER METEOROLOGICAL SOC2023

Limiting the impact of supply chain disruptions in the face of distributional uncertainty in demand

Ralf Seifert, Anna Timonina-Farkas, René Yves Glogg

Service-level requirements play a crucial role in eliminating stock-outs in a production pipeline. However, delivering a specific service level can become an unattainable goal given the various uncertainties influencing both the production pipeline and cus ...

WILEY2022

Analytical prediction of the final error due to noise in a ϕ-OTDR system

Luc Thévenaz, Li Zhang, Malak Mohamed Hossameldeen Omar Mohamed Galal, Yuting Yang, Suneetha Sebastian

An analytical expression to estimate the uncertainty on the frequency shift in ϕ-OTDR systems is established and confirmed by actual data. ...

Optica Publishing Group2022

Measuring the effective thermal resistance of ventilated air-spaces behind common wall assemblies: Theoretical uncertainty analysis and recommendations for the hot box method modifications (ASHRAE 1759-RP)

Dolaana Khovalyg, Mohammad Rahiminejad

A well-designed ventilated wall that incorporates air-space behind cladding can reduce energy use for conditioning buildings by adding a thermally resistive layer. The scarcity of thermal performance data and standardized test methods is a particular barri ...

2022

Learning new physics from an imperfect machine

Andrea Wulzer, Raffaele Tito D'Agnolo

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties a ...

SPRINGER2022

Propagation of fluid driven fractures in transversely isotropic material

Fatima-Ezzahra Moukhtari

The propagation of fluid driven fractures is used in a number of industrial applications (well stimulation of unconventional reservoirs, development of deep geothermal systems) but also occurs naturally (magmatic dyke intrusion). While the mechanics of hyd ...

EPFL2020

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Ahmed Salaheldin Ahmed Shama

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmark ...

EPFL2022