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Related publications (32)

HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

This thesis is devoted to the construction, analysis, and implementation of two types of hierarchical Markov Chain Monte Carlo (MCMC) methods for the solution of large-scale Bayesian Inverse Problems (BIP).The first hierarchical method we present is based ...

EPFL2022

Security Measures for Grids Against Rank-1 Undetectable Time-Synchronization Attacks

Jean-Yves Le Boudec, Marguerite Marie Nathalie Delcourt

Time-synchronization attacks on phasor measurement units (PMUs) pose a real threat to smart grids; it was shown that they are feasible in practice and that they can have a nonnegligible negative impact on state estimation, without triggering the bad data d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Object Priors for Volumetric Image Segmentation

Pamuditha Udaranga Wickramasinghe

Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotat ...

EPFL2022

Predictive models for response to non-invasive brain stimulation in stroke: A critical review of opportunities and pitfalls

Friedhelm Christoph Hummel, Maximilian Jonas Wessel, Philip Egger

Background: Noninvasive brain stimulation has been successfully applied to improve stroke impairments in different behavioral domains. Yet, clinical translation is limited by heterogenous comes within and across studies. It has been proposed to develop and ...

ELSEVIER SCIENCE INC2021

A General Framework for Optimal Data-Driven Optimization

Daniel Kuhn, Tobias Sutter

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

2021

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Amit Sharma

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

Entering the plasma agriculture field: An attempt to standardize protocols for plasma treatment of seeds

Ivo Furno, Alan Howling, Fabio Avino, Alexandra Waskow

Plasma treatments are currently being assessed as a seed processing technology for agricultural purposes. There is sufficient information as a proof-of-concept, but a lack of standardization in the methodology prevents a convincing evaluation of plasma tre ...

WILEY-V C H VERLAG GMBH2021

Pollution Detection Algorithm (PDA)

Julia Schmale, Andrea Baccarini, Ivo Fabio Beck, Hélène Paule Angot

The Pollution Detection Algorithm (PDA) is an algorithm to identify and flag periods of primary polluted data in remote atmospheric time series in five steps. The first and most important step identifies polluted periods based on the gradient (time-derivat ...

EPFL Infoscience2021

Testing general relativity on cosmological scales at redshift z similar to 1.5 with quasar and CMB lensing

Cheng Zhao

We test general relativity (GR) at the effective redshift (z) over tilde similar to 1.5 by estimating the statistic E-G, a probe of gravity, on cosmological scales 19 - 190 h(-1)Mpc. This is the highest redshift and largest scale estimation of E-G so far. ...

OXFORD UNIV PRESS2021

On long time behavior of solutions to nonlinear dispersive equations

Stefano Francesco Burzio

In part I, we address the issue of existence of solutions for Cauchy problems involving nonlinear hyperbolic equations for initial data in Sobolev spaces with scaling subcritical regularity. In particular, we analyse nonlinear estimates for null-forms in t ...

EPFL2020

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