Author name cluster

Elisa Fromont

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Robust Detection of Synthetic Tabular Data Under Schema Variability

G. Charbel N. Kindji
Elisa Fromont
Lina M. Rojas-Barahona
Tanguy Urvoy

The rise of powerful generative models has sparked concerns over data authenticity. While detection methods have been extensively developed for images and text, the case of tabular data, despite its ubiquity, has been largely overlooked. Yet, detecting synthetic tabular data is especially challenging due to its heterogeneous structure and unseen formats at test time. We address the underexplored task of detecting synthetic tabular data "in the wild", i.e. when the detector is deployed on tables with variable and previously unseen schemas. We introduce a novel datum-wise transformer architecture that significantly outperforms the only previously published baseline, improving both AUC and accuracy by 7 points. By incorporating a table-adaptation component, our model gains an additional 7 accuracy points, demonstrating enhanced robustness. This work provides the first strong evidence that detecting synthetic tabular data in real-world conditions is feasible, and demonstrates substantial improvements over previous approaches. The code will be made available in the extended version.

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ECAI Conference 2025 Conference Paper

Plausible Conditional Generation-Based Counterfactual Explanations for Multivariate Times Series Classification

Paul Sevellec
Elisa Fromont
Romaric Gaudel
Laurence Rozé
Matteo Sammarco

Multivariate time series (MTS) are prevalent but inherently complex, making them challenging to analyze due to strong temporal and inter-variable correlations. This complexity often results in the use of sophisticated and difficult-to-interpret machine learning models. In real-life scenarios where critical applications of these models are common, their acceptability is crucial. Counterfactual explanations have emerged as a valuable tool for understanding machine learning systems by providing post-hoc analyzes of classification models. We introduce CFE4MTS (CounterFactual Explanation for Multivariate Time Series), a conditional, generation-based, plausible counterfactual explanation method, specifically designed for multivariate time series classification. Our approach leverages advanced time series modeling techniques to generate interpretable counterfactuals that belong to a given target class distribution. To evaluate the effectiveness of our method, we apply it to various real datasets, demonstrating the superiority of our approach over the state of the art methods.

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ICRA Conference 2023 Conference Paper

Deep metric learning for visual servoing: when pose and image meet in latent space

Samuel Felton
Elisa Fromont
Éric Marchand

We propose a new visual servoing method that controls a robot's motion in a latent space. We aim to extract the best properties of two previously proposed servoing methods: we seek to obtain the accuracy of photometric methods such as Direct Visual Servoing (DVS), as well as the behavior and convergence of pose-based visual servoing (PBVS). Photometric methods suffer from limited convergence area due to a highly non-linear cost function, while PBVS requires estimating the pose of the camera which may introduce some noise and incurs a loss of accuracy. Our approach relies on shaping (with metric learning) a latent space, in which the representations of camera poses and the embeddings of their respective images are tied together. By leveraging the multimodal aspect of this shared space, our control law minimizes the difference between latent image representations thanks to information obtained from a set of pose embeddings. Experiments in simulation and on a robot validate the strength of our approach, showing that the sought out benefits are effectively found.

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ICML Conference 2022 Conference Paper

UniRank: Unimodal Bandit Algorithms for Online Ranking

Camille-Sovanneary Gauthier
Romaric Gaudel
Elisa Fromont

We tackle, in the multiple-play bandit setting, the online ranking problem of assigning L items to K predefined positions on a web page in order to maximize the number of user clicks. We propose a generic algorithm, UniRank, that tackles state-of-the-art click models. The regret bound of this algorithm is a direct consequence of the pseudo-unimodality property of the bandit setting with respect to a graph where nodes are ordered sets of indistinguishable items. The main contribution of UniRank is its O(L/$\Delta$ logT) regret for T consecutive assignments, where $\Delta$ relates to the reward-gap between two items. This regret bound is based on the usually implicit condition that two items may not have the same attractiveness. Experiments against state-of-the-art learning algorithms specialized or not for different click models, show that our method has better regret performance than other generic algorithms on real life and synthetic datasets.

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ICML Conference 2021 Conference Paper

Parametric Graph for Unimodal Ranking Bandit

Camille-Sovanneary Gauthier
Romaric Gaudel
Elisa Fromont
Boammani Aser Lompo

We tackle the online ranking problem of assigning $L$ items to $K$ positions on a web page in order to maximize the number of user clicks. We propose an original algorithm, easy to implement and with strong theoretical guarantees to tackle this problem in the Position-Based Model (PBM) setting, well suited for applications where items are displayed on a grid. Besides learning to rank, our algorithm, GRAB (for parametric Graph for unimodal RAnking Bandit), also learns the parameter of a compact graph over permutations of $K$ items among $L$. The logarithmic regret bound of this algorithm is a direct consequence of the unimodality property of the bandit setting with respect to the learned graph. Experiments against state-of-the-art learning algorithms which also tackle the PBM setting, show that our method is more efficient while giving regret performance on par with the best known algorithms on simulated and real life datasets.

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ICRA Conference 2021 Conference Paper

Siame-se(3): regression in se(3) for end-to-end visual servoing

Samuel Felton
Elisa Fromont
Éric Marchand

In this paper we propose a deep architecture and the associated learning strategy for end-to-end direct visual servoing. The considered approach allows to sequentially predict, in se(3), the velocity of a camera mounted on the robot’s end-effector for positioning tasks. Positioning is achieved with high precision despite large initial errors in both cartesian and image spaces. Training is fully done in simulation, alleviating the burden of data collection. We demonstrate the efficiency of our method in experiments in both simulated and real-world environments. We also show that the proposed approach is able to handle multiple scenes.

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ECAI Conference 2008 Conference Paper

Intelligent adaptive monitoring for cardiac surveillance

Lucie Callens
Guy Carrault
Marie-Odile Cordier
Elisa Fromont
François Portet
Rene Quiniou

Monitoring patients in intensive care units is a critical task. Simple condition detection is generally insufficient to diagnose a patient and may generate many false alarms to the clinician operator. Deeper knowledge is needed to discriminate among alarms those that necessitate urgent therapeutic action. We propose an intelligent monitoring system that makes use of many artificial intelligence techniques: artificial neural networks for temporal abstraction, temporal reasoning, model based diagnosis, decision rule based system for adaptivity and machine learning for knowledge acquisition. To tackle the difficulty of taking context change into account, we introduce a pilot aiming at adapting the system behavior by reconfiguring or tuning the parameters of the system modules. A prototype has been implemented and is currently experimented and evaluated. Some results, showing the benefits of the approach, are given.

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