Publications related to A Newton Frank-Wolfe method for constrained self-concordant minimization

A Combination Technique for Optimal Control Problems Constrained by Random PDEs

We present a combination technique based on mixed differences of both spatial approximations and quadrature formulae for the stochastic variables to solve efficiently a class of optimal control problems (OCPs) constrained by random partial differential equ ...

2024

Stability of Image-Reconstruction Algorithms

Michaël Unser, Sebastian Jonas Neumayer, Pol del Aguila Pla

Robustness and stability of image-reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ( ℓ2 and ℓ1 regulariz ...

2023

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Paul Thierry Yves Rolland

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

EPFL2022

Computational Inverse Design of Surface-based Inflatables

Mark Pauly, Florin Isvoranu, Tian Chen

We present a computational inverse design method for a new class of surface-based inflatable structure. Our deployable structures are fabricated by fusing together two layers of inextensible sheet material along carefully selected curves. The fusing curves ...

ASSOC COMPUTING MACHINERY2021

Large Steps in Inverse Rendering of Geometry

Wenzel Alban Jakob, Baptiste Alexandre Marie Philippe Claude Nicolet

Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to mi ...

ASSOC COMPUTING MACHINERY2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Adaptive Gradient Descent without Descent

Konstantin Mishchenko

We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don’t increase the stepsize too fast and 2) don’t overstep the local curvature. No need for functional values, no line search, no information about the func ...

2020

A Unifying Representer Theorem for Inverse Problems and Machine Learning

Michaël Unser

Regularization addresses the ill-posedness of the training problem in machine learning or the reconstruction of a signal from a limited number of measurements. The method is applicable whenever the problem is formulated as an optimization task. The standar ...

2020

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Loris Silva Brites

The assessment of the risk maps for the seismic vulnerability at large scale is based on the vulnerability of each building. In order to determine these vulnerabilities, it is first required to assign to each building its construction class. The constructi ...

2020

Allocation of Active Power Reserve from Active Distribution Networks Using a Cost-Benefit Approach: Application to Swissgrid Network

Fabrizio Sossan, Mokhtar Bozorg, Mohsen Kalantar Neyestanaki

The flexibility of distributed energy resources (DERs) accommodated in active distribution networks (ADNs) can be aggregated and then used to provide ancillary services to the transmission system. In this context, this paper presents a linear optimization ...

IEEE2019

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

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A Combination Technique for Optimal Control Problems Constrained by Random PDEs

Stability of Image-Reconstruction Algorithms

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Computational Inverse Design of Surface-based Inflatables

Large Steps in Inverse Rendering of Geometry

A first-order primal-dual method with adaptivity to local smoothness

Adaptive Gradient Descent without Descent

A Unifying Representer Theorem for Inverse Problems and Machine Learning

Vulnérabilité sismique à grande échelle de la ville de Lausanne

Allocation of Active Power Reserve from Active Distribution Networks Using a Cost-Benefit Approach: Application to Swissgrid Network

Stability of Image-Reconstruction Algorithms

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Large Steps in Inverse Rendering of Geometry

A first-order primal-dual method with adaptivity to local smoothness

Adaptive Gradient Descent without Descent

Computational Inverse Design of Surface-based Inflatables

A Unifying Representer Theorem for Inverse Problems and Machine Learning

A Combination Technique for Optimal Control Problems Constrained by Random PDEs

Allocation of Active Power Reserve from Active Distribution Networks Using a Cost-Benefit Approach: Application to Swissgrid Network

Vulnérabilité sismique à grande échelle de la ville de Lausanne