425 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR43 papers
- EDF Energy (United Kingdom)GB24 papers
- CERMICSFR17 papers
- Université Paris-SaclayFR17 papers
- Institut de Mathématiques de ToulouseFR16 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR14 papers
- Institut national de recherche en sciences et technologies du numériqueFR14 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR14 papers
- Université Toulouse III - Paul SabatierFR13 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR12 papers
- École PolytechniqueFR12 papers
- Laboratoire de Probabilités et Modèles AléatoiresFR12 papers
16 papers · 1 filter
Fast Gaussian process inference by exact Matérn kernel decomposition
Nicolas Langrené, Xavier Warin, Pierre Gruet
To speed up Gaussian process inference, a number of fast kernel matrix-vector multiplication (MVM) approximation algorithms have been proposed over the years. In this paper, we est…
Conformal Prediction for Hierarchical Data
Guillaume Principato, Gilles Stoltz, Yvenn Amara-Ouali +3
We consider conformal prediction for multivariate data and focus on hierarchical data, where some components are linear combinations of others. Intuitively, the hierarchical struct…
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution
Gabriel Kasmi, Amandine Brunetto, Thomas Fel +1
Feature attribution methods aim to improve the transparency of deep neural networks by identifying the input features that influence a model's decision. Pixel-based heatmaps have b…
Conformal Prediction with Missing Values
Margaux Zaffran, Aymeric Dieuleveut, Julie Josse +1
Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting t…
Bayesian sequential design of computer experiments for quantile set inversion
Romain Ait Abdelmalek-Lomenech, Julien Bect, Vincent Chabridon +1
We consider an unknown multivariate function representing a system-such as a complex numerical simulator-taking both deterministic and uncertain inputs. Our objective is to estimat…
Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary
Cristina Butucea, Jean-François Delmas, Anne Dutfoy +1
In this paper we observe a set, possibly a continuum, of signals corrupted by noise. Each signal is a finite mixture of an unknown number of features belonging to a continuous dict…
Off-the-grid learning of mixtures from a continuous dictionary
Cristina Butucea, Jean-François Delmas, Anne Dutfoy +1
We consider a general non-linear model where the signal is a finite mixture of an unknown, possibly increasing, number of features issued from a continuous dictionary parameterized…
Adaptive Conformal Predictions for Time Series
Margaux Zaffran, Aymeric Dieuleveut, Olivier Féron +2
Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires excha…
Surrogate-based variational data assimilation for tidal modelling
Rem-Sophia Mouradi, Cédric Goeury, Olivier Thual +2
Data assimilation (DA) is widely used to combine physical knowledge and observations. It is nowadays commonly used in geosciences to perform parametric calibration. In a context of…
Making the most of your day: online learning for optimal allocation of time
Etienne Boursier, Tristan Garrec, Vianney Perchet +1
We study online learning for optimal allocation when the resource to be allocated is time. %Examples of possible applications include job scheduling for a computing server, a drive…
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Yuan Yin, Vincent Le Guen, Jérémie Dona +4
Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purel…
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…
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…
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates
Jiali Mei, Yohann De Castro, Yannig Goude +1
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix en…
A Chaining Algorithm for Online Nonparametric Regression
Pierre Gaillard, Sébastien Gerchinovitz
We consider the problem of online nonparametric regression with arbitrary deterministic sequences. Using ideas from the chaining technique, we design an algorithm that achieves a D…
Forecasting electricity consumption by aggregating specialized experts
Marie Devaine, Pierre Gaillard, Yannig Goude +1
We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretic…