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Well-posedness Issues For the Half-Wave Maps Equation With Hyperbolic And Spherical Targets

Yang Liu

This thesis is a study of the global well-posedness of the Cauchy problems for half-wave maps from the Minkowski space of dimension n+1 to the 2-dimensional sphere and the hyperbolic plane. The work is mainly based on the results from Krieger-Sire 17' in ...

EPFL2023

Single-shot Kramers-Kronig complex orbital angular momentum spectrum retrieval

Camille Sophie Brès, Jianqi Hu, Yujie Chen

Orbital angular momentum (OAM) spectrum diagnosis is a fundamental building block for diverse OAM-based systems. Among others, the simple on-axis interferometric measurement can retrieve the amplitude and phase information of complex OAM spectra in a few s ...

SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS2023

Information-driven transitions in projections of underdamped dynamics

Daniel Maria Busiello, Giorgio Nicoletti, Amos Maritan

Low-dimensional representations of underdamped systems often provide useful insights and analytical tractability. Here, we build such representations via information projections, obtaining an optimal model that captures the most information on observed spa ...

AMER PHYSICAL SOC2022

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Damir Filipovic

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. Our method learns the features necessary for an effective low-dimensi ...

WILEY2022

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Soham Sarkar

In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite poorly if differences between locations of the underlying populations get m ...

Microtome Publishing2022

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Andreas Loukas, Nikolaos Karalias

Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (I) not naturally amenable to gradient-based optimization, and (II) incompatible with deep lear ...

2022

Sharp asymptotics on the compression of two-layer neural networks

Marco Mondelli

In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimiz ...

IEEE2022

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Ketson Roberto Maximiano Dos Santos

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

2022

Genuinely distributed Byzantine machine learning

Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

2022

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Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Ketson Roberto Maximiano Dos Santos

2022