11 papers
High-dimensional reliability-oriented Shapley effect estimation with Normalizing Flows
Lucas Monteiro, Jérôme Morio, Julien Demange-Chryst +1
This article presents a new estimation scheme for the reliability-oriented Shapley effects when there is a large number of correlated input variables in the model, using a unique s…
Wasserstein Spatial Depth
François Bachoc, Alberto González-Sanz, Jean-Michel Loubes +1
Modeling observations as random distributions embedded within Wasserstein spaces is becoming increasingly popular across scientific fields, as it captures the variability and geome…
A Parametric Contextual Online Learning Theory of Brokerage
François Bachoc, Tommaso Cesari, Roberto Colomboni
We study the role of contextual information in the online learning problem of brokerage between traders. In this sequential problem, at each time step, two traders arrive with secr…
Online Budget Allocation with Censored Semi-Bandit Feedback
François Bachoc, Nicolò Cesa-Bianchi, Tommaso Cesari +1
We study a stochastic budget-allocation problem over tasks. At each round , the learner chooses an allocation . Task succeeds with probability $F_k(X_{t,k}…
Selective inference after convex clustering with penalization
François Bachoc, Cathy Maugis-Rabusseau, Pierre Neuvial
Classical inference methods notoriously fail when applied to data-driven test hypotheses or inference targets. Instead, dedicated methodologies are required to obtain statistical g…
Scale estimation and rate-unbiasedness for Gaussian processes under smoothness misspecification
Toni Karvonen, François Bachoc
Gaussian process regression is used throughout statistics and machine learning for prediction and uncertainty quantification. A Gaussian process is specified by its mean and covari…