Publications related to A Unifying Representer Theorem for Inverse Problems and Machine Learning

A Newton Frank-Wolfe method for constrained self-concordant minimization

We develop a new Newton Frank-Wolfe algorithm to solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO). Unlike L-smooth convex functions, where the Lipschitz continuity of the objective gradient holds gl ...

SPRINGER2021

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Jean-Philippe Thiran, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Marco Pizzolato, Gian Franco Piredda, Thomas Yu, Alessandro Daducci, Nicolas Kunz

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is ...

2021

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

2021

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Enrico Amico

Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential ...

MARY ANN LIEBERT, INC2021

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Wasserstein Adversarial Regularization for learning with label noise

Devis Tuia, Sylvain Lobry, Nicolas Courty

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose ...

2021

Joint Graph-based Depth Refinement and Normal Estimation

Pascal Frossard, Mireille El Gheche, Mattia Rossi

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

2020

Good Vibrations and Unknown Excitations

Dalia Salem Hassan Fahmy El Badawy

Inspired by the human ability to localize sounds, even with only one ear, as well as to recognize objects using active echolocation, we investigate the role of sound scattering and prior knowledge in regularizing ill-posed inverse problems in acoustics. In ...

EPFL2020

Functional Penalised Basis Pursuit on Spheres

Matthieu Martin Jean-André Simeoni

In this work, we propose a unified theoretical and practical spherical approximation framework for functional inverse problems on the hypersphere. More specifically, we consider recovering spherical fields directly in the continuous domain using functional ...

2020

Scalable Convex Optimization Methods for Semidefinite Programming

Alp Yurtsever

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, contemporary applications in science and engineering impose heavy computational and storage burdens on the optimization algorithms. As a result, there ...

EPFL2019

A Unifying Representer Theorem for Inverse Problems and Machine Learning

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A Newton Frank-Wolfe method for constrained self-concordant minimization

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Convergence of adaptive algorithms for constrained weakly convex optimization

Wasserstein Adversarial Regularization for learning with label noise

Joint Graph-based Depth Refinement and Normal Estimation

Good Vibrations and Unknown Excitations

Functional Penalised Basis Pursuit on Spheres

Scalable Convex Optimization Methods for Semidefinite Programming

A Newton Frank-Wolfe method for constrained self-concordant minimization

Wasserstein Adversarial Regularization for learning with label noise

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Good Vibrations and Unknown Excitations

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Joint Graph-based Depth Refinement and Normal Estimation

Scalable Convex Optimization Methods for Semidefinite Programming

Convergence of adaptive algorithms for constrained weakly convex optimization

Functional Penalised Basis Pursuit on Spheres