479 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 ScientifiqueFR192 papers
- Université Paris-SaclayFR78 papers
- Université de MontpellierFR75 papers
- AgroParisTechFR59 papers
- Centre de Coopération Internationale en Recherche Agronomique pour le DéveloppementFR56 papers
- Mathématiques, Informatique et Statistique pour l'Environnement et l'AgronomieFR44 papers
- Institut de Recherche pour le DéveloppementFR41 papers
- Institut National de la Recherche AgronomiqueMA39 papers
- Institut Agro MontpellierFR38 papers
- Institut national de recherche en sciences et technologies du numériqueFR38 papers
- Territoires, Environnement, Télédétection et Information SpatialeFR33 papers
- Université Grenoble AlpesFR32 papers
18 papers · 1 filter
Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting
Ferdinand Bhavsar, Lionel Benoit, Maxime Savatier +1
The modeling of hydrometeorological time series with limited observations is a key challenge in the monitoring of hydro-systems and water resources, as well as for flood or drought…
Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
Gregory Beurier, Robin Reiter, Camille Noûs +2
Preprocessing screening is often the most expensive part of a near-infrared spectroscopy calibration workflow. It works because smoothing, derivatives, detrending and related filte…
Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
Bertrand Cloez, Adrien Cotil, Jean-Baptiste Menassol +1
We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in…
Phase Transition for Stochastic Block Model with more than Communities
Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen
Predictions from statistical physics postulate that recovery of the communities in the Stochastic Block Model (SBM) with a fixed number of communities is possible in polynomial…
Low-degree lower bounds via almost orthonormal bases
Alexandra Carpentier, Simone Maria Giancola, Christophe Giraud +1
Low-degree polynomials have emerged as a powerful paradigm for providing evidence of statistical--computational gaps across a variety of high-dimensional statistical models [Wein25…
SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search
Leonardo Martins Bianco, Christine Keribin, Zacharie Naulet
Community detection is a fundamental task in graph analysis, with methods often relying on fitting models like the Stochastic Block Model (SBM) to observed networks. While many alg…
Interpretability of Graph Neural Networks to Assess Effects of Global Change Drivers on Ecological Networks
Emre Anakok, Pierre Barbillon, Colin Fontaine +1
Pollinators play a crucial role for plant reproduction, either in natural ecosystem or in human-modified landscape. Global change drivers,including climate change or land use modif…
Active clustering with bandit feedback
Victor Thuot, Alexandra Carpentier, Christophe Giraud +1
We investigate the Active Clustering Problem (ACP). A learner interacts with an -armed stochastic bandit with -dimensional subGaussian feedback. There exists a hidden partiti…
Active Ranking of Experts Based on their Performances in Many Tasks
El Mehdi Saad, Nicolas Verzelen, Alexandra Carpentier
We consider the problem of ranking n experts based on their performances on d tasks. We make a monotonicity assumption stating that for each pair of experts, one outperforms the ot…
Covariance Adaptive Best Arm Identification
El Mehdi Saad, Gilles Blanchard, Nicolas Verzelen
We consider the problem of best arm identification in the multi-armed bandit model, under fixed confidence. Given a confidence input , the goal is to identify the arm with the h…
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output. We derive learning bounds for our method, and study und…
An active learning approach for improving the performance of equilibrium based chemical simulations
Mary Savino, Céline Lévy-Leduc, Marc Leconte +1
In this paper, we propose a novel sequential data-driven method for dealing with equilibrium based chemical simulations, which can be seen as a specific machine learning approach c…
Learning Output Embeddings in Structured Prediction
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means…
Stratified cross-validation for unbiased and privacy-preserving federated learning
R. Bey, R. Goussault, M. Benchoufi +1
Large-scale collections of electronic records constitute both an opportunity for the development of more accurate prediction models and a threat for privacy. To limit privacy expos…
X-Armed Bandits: Optimizing Quantiles, CVaR and Other Risks
Léonard Torossian, Aurélien Garivier, Victor Picheny
We propose and analyze StoROO, an algorithm for risk optimization on stochastic black-box functions derived from StoOO. Motivated by risk-averse decision making fields like agricul…
Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data
Ibrahim Fayad, Nicolas Baghdadi, Stéphane Guitet +5
Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar…
Reliable ABC model choice via random forests
Pierre Pudlo, Jean-Michel Marin, Arnaud Estoup +3
Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulat…
Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models
Pierre Fernique, Jean-Baptiste Durand, Yann Guédon
Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The anal…