28 citations · 55 across the 24 of their papers we have counts for
26 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…
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})…
Inference post region selection
Dominique Bontemps, François Bachoc, Pierre Neuvial
Post-selection inference consists in providing statistical guarantees, based on a data set, that are robust to a prior model selection step on the same data set. In this paper, we…
A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage
François Bachoc, Tommaso Cesari, Roberto Colomboni
We study a contextual version of the repeated brokerage problem. In each interaction, two traders with private valuations for an item seek to buy or sell based on the learner's-a b…
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…
Fair Online Bilateral Trade
François Bachoc, Nicolò Cesa-Bianchi, Tommaso Cesari +1
In online bilateral trade, a platform posts prices to incoming pairs of buyers and sellers that have private valuations for a certain good. If the price is lower than the buyers' v…
Asymptotic analysis for covariance parameter estimation of Gaussian processes with functional inputs
Lucas Reding, Andrés F. López-Lopera, François Bachoc
We consider covariance parameter estimation for Gaussian processes with functional inputs. From an increasing-domain asymptotics perspective, we prove the asymptotic consistency an…
Improved learning theory for kernel distribution regression with two-stage sampling
François Bachoc, Louis Béthune, Alberto González-Sanz +1
The distribution regression problem encompasses many important statistics and machine learning tasks, and arises in a large range of applications. Among various existing approaches…
Gaussian Processes on Distributions based on Regularized Optimal Transport
François Bachoc, Louis Béthune, Alberto Gonzalez-Sanz +1
We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport. We propose an Hilbertian embedding of the space of…
Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals
François Bachoc, Christoph Muehlmann, Klaus Nordhausen +1
Non-stationary source separation is a well-established branch of blind source separation with many different methods. However, for none of these methods large-sample results are av…
Regret Analysis of Dyadic Search
François Bachoc, Tommaso Cesari, Roberto Colomboni +1
We analyze the cumulative regret of the Dyadic Search algorithm of Bachoc et al. [2022].
A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization
François Bachoc, Tommaso Cesari, Roberto Colomboni +1
This paper studies a natural generalization of the problem of minimizing a univariate convex function by querying its values sequentially. At each time-step , the optimizer…
Multivariate Gaussian Random Fields over Generalized Product Spaces involving the Hypertorus
François Bachoc, Ana Peron, Emilio Porcu
The paper deals with multivariate Gaussian random fields defined over generalized product spaces that involve the hypertorus. The assumption of Gaussianity implies the finite dimen…
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…
Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates
François Bachoc, Tommaso R Cesari, Sébastien Gerchinovitz
We study the problem of zeroth-order (black-box) optimization of a Lipschitz function defined on a compact subset of , with the additional constraint…
Sequential construction and dimension reduction of Gaussian processes under constraints
François Bachoc, Andrés F. López Lopera, Olivier Roustant
Accounting for inequality constraints, such as boundedness, monotonicity or convexity, is challenging when modeling costly-to-evaluate black box functions. In this regard, finite-d…
Asymptotically Equivalent Prediction in Multivariate Geostatistics
François Bachoc, Emilio Porcu, Moreno Bevilacqua +2
Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geostatistics. While best linear prediction has been well understood in un…
Gaussian linear approximation for the estimation of the Shapley effects
Baptiste Broto, François Bachoc, Marine Depecker +1
In this paper, we address the estimation of the sensitivity indices called "Shapley eects". These sensitivity indices enable to handle dependent input variables. The Shapley eects…
Sequential Design of Mixture Experiments with an Empirically Determined Input Domain and an Application to Burn-up Credit Penalization of Nuclear Fuel Rods
François Bachoc, Théo Barthe, Thomas Santner +1
This paper proposes a sequential design for maximizing a stochastic computer simulator output, y(x), over an unknown optimization domain. The training data used to estimate the opt…
Asymptotic properties of the maximum likelihood and cross validation estimators for transformed Gaussian processes
François Bachoc, José Bétancourt, Reinhard Furrer +1
The asymptotic analysis of covariance parameter estimation of Gaussian processes has been subject to intensive investigation. However, this asymptotic analysis is very scarce for n…
Spatial Blind Source Separation
François Bachoc, Marc G. Genton, Klaus Nordhausen +2
Recently a blind source separation model was suggested for spatial data together with an estimator based on the simultaneous diagonalisation of two scatter matrices. The asymptotic…
Semi-parametric estimation of the variogram of a Gaussian process with stationary increments
Jean-Marc Azaïs, François Bachoc, Agnès Lagnoux +1
We consider the semi-parametric estimation of a scale parameter of a one-dimensional Gaussian process with known smoothness. We suggest an estimator based on quadratic variations a…
Gaussian Processes indexed on the symmetric group: prediction and learning
François Bachoc, Baptiste Broto, Fabrice Gamboa +1
In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X. The Euclidean case…