19 citations · 62 across the 15 of their papers we have counts for
10 papers · 1 filter
Robust Detection of Synthetic Tabular Data under Schema Variability
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
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…
Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
The growing power of generative models raises major concerns about the authenticity of published content. To address this problem, several synthetic content detection methods have…
Synthetic Tabular Data Detection In the Wild
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores wh…
Cross-table Synthetic Tabular Data Detection
G. Charbel N. Kindji, Lina Maria Rojas-Barahona, Elisa Fromont +1
Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores wh…
Tabular Data Generation Models: An In-Depth Survey and Performance Benchmarks with Extensive Tuning
G. Charbel N. Kindji, Lina Maria Rojas-Barahona, Elisa Fromont +1
The ability to train generative models that produce realistic, safe and useful tabular data is essential for data privacy, imputation, oversampling, explainability or simulation. H…
Budgeted Reinforcement Learning in Continuous State Space
Nicolas Carrara, Edouard Leurent, Romain Laroche +3
A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemen…
Corrupt Bandits for Preserving Local Privacy
Pratik Gajane, Tanguy Urvoy, Emilie Kaufmann
We study a variant of the stochastic multi-armed bandit (MAB) problem in which the rewards are corrupted. In this framework, motivated by privacy preservation in online recommender…
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
Pratik Gajane, Tanguy Urvoy, Fabrice Clérot
We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the se…
Utility-based Dueling Bandits as a Partial Monitoring Game
Pratik Gajane, Tanguy Urvoy
Partial monitoring is a generic framework for sequential decision-making with incomplete feedback. It encompasses a wide class of problems such as dueling bandits, learning with ex…
Random Forest for the Contextual Bandit Problem - extended version
Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy +1
To address the contextual bandit problem, we propose an online random forest algorithm. The analysis of the proposed algorithm is based on the sample complexity needed to find the…