Publications related to Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

2024

On the fast assemblage of finite element matrices with application to nonlinear heat transfer problems

The finite element method is a well-established method for the numerical solution of partial differential equations (PDEs), both linear and nonlinear. However, the repeated re -assemblage of finite element matrices for nonlinear PDEs is frequently pointed ...

ELSEVIER SCIENCE INC2023

A model for the consolidation of hybrid textiles considering air entrapment, dissolution and diffusion

Véronique Michaud, Vincent Werlen, Christian Rytka

A new model is proposed for the consolidation of hybrid textiles, in which air entrapment and dissolution are considered. One of the key parameters is tow permeability, which is described by the analytical model of Gebart and validated at very high fibre v ...

ELSEVIER SCI LTD2023

Biobased surfactants

Jeremy Luterbacher, Songlan Sun, Stefania Bertella, Anastasiia Komarova

Compound of the general formula (Ia), (Ib) and (Ic) R50 and R60 are different form each other and are selected from the group consisting of -R70, -ZR70, -Z-OH, -Z-NH2, -Z-SH, -Z-OC(O)R70, -OC(O)R70, - COOH and its corresponding salts, -C(O)NH2, -C(O)NH-R70 ...

2023

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Nicolas Henri Bernard Flammarion, Scott William Pesme, Mathieu Even

In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over diagonal linear networks. We prove the convergence of GD and SGD with macroscop ...

2023

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Scott William Pesme

In this paper we fully describe the trajectory of gradient flow over diagonal linear networks in the limit of vanishing initialisation. We show that the limiting flow successively jumps from a saddle of the training loss to another until reaching the minim ...

2023

System and method for transcoding spectral data with task-based optimization via symmetric non-negative matrix factorization

Forrest Simon Webler

A computer-implemented method for reconstructing/recovering high-resolution visible light spectral data at a target resolution d, that comprises obtaining a configuration of a low- resolution multi-channel imaging sensor of resolution p, the configuration ...

2022

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

Efficient Learning of a Linear Dynamical System with Stability Guarantees

Daniel Kuhn, Wouter Jongeneel, Tobias Sutter

We propose a principled method for projecting an arbitrary square matrix to the non- convex set of asymptotically stable matrices. Leveraging ideas from large deviations theory, we show that this projection is optimal in an information-theoretic sense and ...

2021

New Results in Integer and Lattice Programming

Christoph Hunkenschröder

An integer program (IP) is a problem of the form

\min \{f(x) : \, Ax = b, \ l \leq x \leq u, \ x \in \Z^n\}

, where

A \in \Z^{m \times n}

,

b \in \Z^m

,

l,u \in \Z^n

, and

f: \Z^n \rightarrow \Z

is a separable convex objective function. The problem o ...

EPFL2020

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

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High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

On the fast assemblage of finite element matrices with application to nonlinear heat transfer problems

A model for the consolidation of hybrid textiles considering air entrapment, dissolution and diffusion

Biobased surfactants

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

System and method for transcoding spectral data with task-based optimization via symmetric non-negative matrix factorization

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Efficient Learning of a Linear Dynamical System with Stability Guarantees

New Results in Integer and Lattice Programming

On the fast assemblage of finite element matrices with application to nonlinear heat transfer problems

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Biobased surfactants

Efficient Learning of a Linear Dynamical System with Stability Guarantees

New Results in Integer and Lattice Programming

A model for the consolidation of hybrid textiles considering air entrapment, dissolution and diffusion

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

System and method for transcoding spectral data with task-based optimization via symmetric non-negative matrix factorization