output
20042026
most citedA regression-based Monte Carlo method to solve backward stochastic differential equations

425 citations

Showing 2017Show all

14 papers · 1 filter

cs.CY2017★ 18 cited

Novel paradigms for advanced distribution grid energy management

José Horta, Daniel Kofman, David Menga

The electricity distribution grid was not designed to cope with load dynamics imposed by high penetration of electric vehicles, neither to deal with the increasing deployment of di…

stat.CO2017★ 52 cited

Fast and stable multivariate kernel density estimation by fast sum updating

Nicolas Langrené, Xavier Warin

Kernel density estimation and kernel regression are powerful but computationally expensive techniques: a direct evaluation of kernel density estimates at evaluation points give…

math.OC2017★ 17 cited

Demand Side Management in the Smart Grid: an Efficiency and Fairness Tradeoff

Paulin Jacquot, Olivier Beaude, Stéphane Gaubert +1

We compare two Demand Side Management (DSM) mechanisms, introduced respectively by Mohsenian-Rad et al (2010) and Baharlouei et al (2012), in terms of efficiency and fairness. Each…

stat.ML2017★ 36 cited

Introducing machine learning for power system operation support

Benjamin Donnot, Isabelle Guyon, Marc Schoenauer +2

We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with theaim of increasing security and reducing c…

stat.ML2017

Nonnegative matrix factorization with side information for time series recovery and prediction

Jiali Mei, Yohann De Castro, Yannig Goude +2

Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row…

cs.CE2017★ 45 cited

Hybrid High-Order methods for finite deformations of hyperelastic materials

Mickaël Abbas, Alexandre Ern, Nicolas Pignet

We devise and evaluate numerically Hybrid High-Order (HHO) methods for hyperelastic materials undergoing finite deformations. The HHO methods use as discrete unknowns piecewise pol…

math.OC2017★ 66 cited

Log-barrier interior point methods are not strongly polynomial

Xavier Allamigeon, Pascal Benchimol, Stéphane Gaubert +1

We prove that primal-dual log-barrier interior point methods are not strongly polynomial, by constructing a family of linear programs with inequalities in dimension for…

math.ST2017★ 3 cited

Sensitivity analysis using perturbed-law based indices for quantiles and application to an industrial case

Roman Sueur, Bertrand Iooss, Thibault Delage

In this paper, we present perturbed law-based sensitivity indices and how to adapt them for quantile-oriented sensitivity analysis. We exhibit a simple way to compute these indices…

stat.CO2017

Monte-Carlo acceleration: importance sampling and hybrid dynamic systems

H. Chraibi, A. Dutfoy, T. Galtier +1

The reliability of a complex industrial system can rarely be assessed analytically. As system failure is often a rare event, crude Monte-Carlo methods are prohibitively expensive f…

stat.ME2017★ 155 cited

Fast calibrated additive quantile regression

M. Fasiolo, S. N. Wood, M. Zaffran +2

We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation…

math.ST2017★ 30 cited

Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes

Bertrand Iooss, Loïc Le Gratiet

A functional risk curve gives the probability of an undesirable event as a function of the value of a critical parameter of a considered physical system. In several applicative sit…

cs.SE2017★ 12 cited

A Benchmark on Reliability of Complex Discrete Systems: Emergency Power Supply of a Nuclear Power Plant

Marc Bouissou

This paper contains two parts: the description of a real electrical system, with many redundancies, reconfigurations and repairs, then the description of a reliability model of thi…

math.ST2017★ 4 cited

Optimal compromise between incompatible conditional probability distributions, with application to Objective Bayesian Kriging

Joseph Muré

Models are often defined through conditional rather than joint distributions, but it can be difficult to check whether the conditional distributions are compatible, i.e. whether th…

stat.AP2017

Probabilistic risk bounds for the characterization of radiological contamination

Géraud Blatman, Thibault Delage, Bertrand Iooss +1

The radiological characterization of contaminated elements (walls, grounds, objects) from nuclear facilities often suffers from a too small number of measurements. In order to dete…