Publication
Effects of service loading on the behaviour of a suspension bridge
Publications associées (72)
Distributional Regression and Autoregression via Optimal Transport
We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of . Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...
EPFL2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui
We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...
2023Distribution-on-distribution regression via optimal transport maps
Victor Panaretos, Laya Ghodrati
We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation, and links the conditional Frechet mean of the resp ...
OXFORD UNIV PRESS2022Minimax rate for optimal transport regression between distributions
Victor Panaretos, Laya Ghodrati
Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...
ELSEVIER2022One Fuzz Doesn’t Fit All: Optimizing Directed Fuzzing via Target-tailored Program State Restriction
Fuzzing is the de-facto default technique to discover software flaws, randomly testing programs to discover crashing test cases. Yet, a particular scenario may only care about specific code regions (for, e.g., bug reproduction, patch or regression testing) ...
ASSOC COMPUTING MACHINERY2022Linear regression analysis of regional mean speed of Athens city network using drone data: A multi-modal approach
Nikolaos Geroliminis, Emmanouil Barmpounakis
The work proposes a multi-modal regional mean speed regression analysis for the city network of Athens, Greece. The dataset from pNUEMA experiment is used in the present context. Accumulations and mean speeds of different modes are estimated and compared t ...
2021Gaussian Process Regression for Materials and Molecules
Michele Ceriotti, David Mark Wilkins
We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...
AMER CHEMICAL SOC2021Neural controlled differential equations for crop classification
Accurate and scalable crop classification is important for food security and sustainable resources management. The temporal development of crops, i.e., their phenology, is a continuous phenomena that if properly captured, can help to discern them. The nove ...
2021Deep Learning with Convolutional Neural Network for Proportional Control of Finger Movements from surface EMG Recordings
Silvestro Micera, Vincent Alexandre Mendez, Leonardo Pollina, Fiorenzo Artoni
The control of robotic prosthetic hands (RPHs) for upper limb amputees is far from optimal. Simultaneous and proportional finger control of a RPH based on EMG signals is still challenging. Based on EMG and kinematics recordings of subjects following a pre- ...
IEEE2021Sparsest piecewise-linear regression of one-dimensional data
Michaël Unser, Julien René Pierre Fageot, Thomas Jean Debarre, Quentin Alain Denoyelle
We study the problem of one-dimensional regression of data points with total-variation (TV) regularization (in the sense of measures) on the second derivative, which is known to promote piecewise-linear solutions with few knots. While there are efficient a ...
Elsevier2021