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Publications associées (31)

Locally active globally stable dynamical systems: Theory, learning, and experiments

Aude Billard, Nadia Barbara Figueroa Fernandez

State-dependent dynamical systems (DSs) offer adaptivity, reactivity, and robustness to perturbations in motion planning and physical human-robot interaction tasks. Learning DS-based motion plans from non-linear reference trajectories is an active research ...

SAGE PUBLICATIONS LTD2022

Dynamical low rank approximation for uncertainty quantification of time-dependent problems

Eva Vidlicková

The quantification of uncertainties can be particularly challenging for problems requiring long-time integration as the structure of the random solution might considerably change over time. In this respect, dynamical low-rank approximation (DLRA) is very a ...

EPFL2022

Optimal lower bounds on hitting probabilities for non-linear systems of stochastic fractional heat equations

Robert Dalang, Fei Pu

We consider a system of d non-linear stochastic fractional heat equations in spatial dimension 1 driven by multiplicative d-dimensional space-time white noise. We establish a sharp Gaussian-type upper bound on the two-point probability density function of ...

ELSEVIER2021

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Erixhen Sula

Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular network ...

EPFL2021

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 ...

Zenodo2021

Deterministic error bounds for kernel-based learning techniques under bounded noise

Colin Neil Jones, Paul Scharnhorst, Emilio Maddalena

We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and epsilon-support vector regression. By assuming the ground-truth function belongs to th ...

2021

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

Emanuele Mingione, Diego Alberici

A class of deep Boltzmann machines is considered in the simplified framework of a quenched system with Gaussian noise and independent entries. The quenched pressure of a K-layers spin glass model is studied allowing interactions only among consecutive laye ...

2021

Common Information Components Analysis

Michael Christoph Gastpar, Erixhen Sula

Wyner's common information is a measure that quantifies and assesses the commonality between two random variables. Based on this, we introduce a novel two-step procedure to construct features from data, referred to as Common Information Components Analysis ...

2021

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

Climate change scenarios at hourly time‐step over Switzerland from an enhanced temporal downscaling approach

Varun Sharma, Michael Lehning, Wolf Hendrik Huwald, Adrien Michel

Many physically‐based models for climate change impact studies require sub‐daily temporal resolution of the forcing data to provide meaningful predictions. However, climate scenarios are typically available at daily time step, severely limiting the applica ...

2021

Weak-lensing mass reconstruction using sparsity and a Gaussian random field

Austin Chandler Peel

Aims. We introduce a novel approach to reconstructing dark matter mass maps from weak gravitational lensing measurements. The cornerstone of the proposed method lies in a new modelling of the matter density field in the Universe as a mixture of two compone ...

2021

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