Publications

Mathematics

Decentralized Learning in Online Queuing Systems

Motivated by packet routing in computer networks, online queuing systems are composed of queues receiving packets at different rates. Repeatedly, they send packets to servers, each of them treating only at most one packet at a time. In the…

Flore Sentenac, Etienne Boursier, Vianney Perchet

NeurIPS - 2021

Mathematics

Making the most of your day: online learning for optimal allocation of time

We study online learning for optimal allocation when the resource to be allocated is time. Examples of possible applications include a driver filling a day with rides, a landlord renting an estate, etc. Following our initial motivation, a driver…

Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini

NeurIPS - 2021

Neurobiology

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex

A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general…

Anton V Chizhov, Lyle J Graham

PLoS Computational Biology - 2021

Mathematics

Single pass computation of first seismic wave travel time in three dimensional heterogeneous media with general anisotropy

We present a numerical method for computing the first arrival time of seismic waves in media defined by a general Hooke tensor, in contrast with previous methods which are limited to a specific subclass such as the "tilted transversally isotropic" …

François Desquilbet, Jian Cao, Paul Cupillard, Ludovic Métivier, Jean-Marie Mirebeau

Journal of Scientific Computing - 2021

Computer Science and Game Theory

Sequential Learning in a strategical environment

In sequential learning (or repeated games), data is acquired and treated on the fly and an algorithm (or strategy) learns to behave as well as if it got in hindsight the state of nature, e.g., distributions of rewards. In many real life scenarios,…

Etienne Boursier

Machine Learning

Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking

The ROC curve is the gold standard for measuring the performance of a test/scoring statistic regarding its capacity to discriminate between two statistical populations in a wide variety of applications, ranging from anomaly detection in signal…

Stéphan Clémençon, Myrto Limnios, Nicolas Vayatis

Electronic Journal of Statistics - 2021

Modeling and Simulation

Numerical method for the computation of first-arrival time of seismic waves with general anisotropy

We present a numerical method for computing the first arrival time of seismic waves. The scheme can tackle three dimensional media with elastic properties defined by a fully anisotropic Hooke tensor. Our numerical method proceeds in a single pass…

François Desquilbet, Ludovic Métivier, Jean-Marie Mirebeau

First International Meeting for Applied Geoscience & Energy - 2021

Classical Analysis and ODEs

Network Reconstruction Problem for an Epidemic Reaction-Diffusion

We study the network reconstruction problem for an epidemic reaction-diffusion. These models are an extension of deterministic, compartmental models to a graph setting, where the reactions within the nodes are coupled by a diffusion. We study the…

Louis-Brahim Beaufort, Pierre-Yves Massé, Antonin Reboulet, Laurent Oudre

Machine Learning

Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics

The ability to collect and store ever more massive databases has been accompanied by the need to process them efficiently. In many cases, most observations have the same behavior, while a probable small proportion of these observations are abnormal…

Myrto Limnios, Nathan Noiry, Stéphan Clémençon

Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications (2021) - 2021

Statistics

Adversarial Weighting for Domain Adaptation in Regression

We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift. The approach developed in this paper is based on the assumption that the task on the target…

Antoine de Mathelin, Guillaume Richard, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) - 2021