298 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de LilleFR364 papers
- Centre National de la Recherche ScientifiqueFR257 papers
- École Centrale de LilleFR212 papers
- Centre de Recherche en InformatiqueFR203 papers
- Université Paris-SaclayFR74 papers
- Institut d'Astrophysique SpatialeFR64 papers
- Sorbonne UniversitéFR59 papers
- Institut de Recherche en Astrophysique et PlanétologieFR58 papers
- Université Paris CitéFR57 papers
- Université Paris Sciences et LettresFR53 papers
- Ludwig-Maximilians-Universität MünchenDE52 papers
- CEA Paris-SaclayFR51 papers
13 papers · 2 filters
PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs
Deividas Eringis, John Leth, Zheng-Hua Tan +2
In this paper, we derive a PAC-Bayes bound on the generalisation gap, in a supervised time-series setting for a special class of discrete-time non-linear dynamical systems. This cl…
Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework
Florian Felten, El-Ghazali Talbi, Grégoire Danoy
Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in…
Hyperparameter Optimization for Multi-Objective Reinforcement Learning
Florian Felten, Daniel Gareev, El-Ghazali Talbi +1
Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further e…
Leveraging Data Geometry to Mitigate CSM in Steganalysis
Rony Abecidan, Vincent Itier, Jérémie Boulanger +2
In operational scenarios, steganographers use sets of covers from various sensors and processing pipelines that differ significantly from those used by researchers to train stegana…
Breiman meets Bellman: Non-Greedy Decision Trees with MDPs
Hector Kohler, Riad Akrour, Philippe Preux
In supervised learning, decision trees are valued for their interpretability and performance. While greedy decision tree algorithms like CART remain widely used due to their comput…
Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study
Patrick Saux, Pierre Bauvin, Violeta Raverdy +31
Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develo…
PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs
Dániel Rácz, Mihály Petreczky, Bálint Daróczy
We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time. LPV system…
Pure Exploration in Bandits with Linear Constraints
Emil Carlsson, Debabrota Basu, Fredrik D. Johansson +1
We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when \emph{the arms are subject to linear constraints}. Unlike…
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
Timothée Mathieu, Riccardo Della Vecchia, Alena Shilova +4
Recently, the scientific community has questioned the statistical reproducibility of many empirical results, especially in the field of machine learning. To contribute to the resol…
Optimal Interpretability-Performance Trade-off of Classification Trees with Black-Box Reinforcement Learning
Hector Kohler, Riad Akrour, Philippe Preux
Interpretability of AI models allows for user safety checks to build trust in these models. In particular, decision trees (DTs) provide a global view on the learned model and clear…
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Hannes Eriksson, Debabrota Basu, Tommy Tram +2
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it a…
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
Edwige Cyffers, Aurélien Bellet, Debabrota Basu
We study differentially private (DP) machine learning algorithms as instances of noisy fixed-point iterations, in order to derive privacy and utility results from this well-studied…
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data
Pratik Karmakar, Debabrota Basu
We study design of black-box model extraction attacks that can send minimal number of queries from a publicly available dataset to a target ML model through a predictive API with a…