174 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Institut national de recherche en sciences et technologies du numériqueFR146 papers
- Université de LilleFR64 papers
- Centre National de la Recherche ScientifiqueFR49 papers
- Centre de Recherche en Informatique, Signal et Automatique de LilleFR41 papers
- Centre de Recherche en InformatiqueFR30 papers
- École Centrale de LilleFR19 papers
- Laboratoire d'Informatique Fondamentale de LilleFR18 papers
- Laboratoire Paul PainlevéFR17 papers
- University College LondonGB16 papers
- Université Libre de BruxellesBE11 papers
- Université Paris-SaclayFR11 papers
- University of LondonGB9 papers
20 papers · 1 filter
IDEQ -- Improving Diffusion Models for the Traveling Salesman Problem (TSP) by Leveraging the Structure of the Solution Space
Mickael Basson, Philippe Preux
We investigate diffusion models to solve the Traveling Salesman Problem. Building on the recent DIFUSCO and T2TCO approaches, we propose IDEQ. IDEQ improves the quality of the solu…
Learning via Surrogate PAC-Bayes
Antoine Picard-Weibel, Roman Moscoviz, Benjamin Guedj
PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalis…
Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs
Chao Han, Debabrota Basu, Michael Mangan +2
Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov D…
AI for Explosive Ordnance Detection in Clearance Operations: The State of Research
Björn Kischelewski, Gregory Cathcart, David Wahl +1
The detection and clearance of explosive ordnance (EO) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its effi…
SoK: Verifiable Cross-Silo FL
Aleksei Korneev, Jan Ramon
Federated Learning (FL) is a widespread approach that allows training machine learning (ML) models with data distributed across multiple devices. In cross-silo FL, which often appe…
Active Fourier Auditor for Estimating Distributional Properties of ML Models
Ayoub Ajarra, Bishwamittra Ghosh, Debabrota Basu
With the pervasive deployment of Machine Learning (ML) models in real-world applications, verifying and auditing properties of ML models have become a central concern. In this work…
Testing Credibility of Public and Private Surveys through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Testing whether a sample survey is a credible representation of the population is an important question to ensure the validity of any downstream research. While this problem, in ge…
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints
Udvas Das, Debabrota Basu
Pure exploration in bandits formalises multiple real-world problems, such as tuning hyper-parameters or conducting user studies to test a set of items, where different safety, reso…
Predicting Electricity Consumption with Random Walks on Gaussian Processes
Chloé Hashimoto-Cullen, Benjamin Guedj
We consider time-series forecasting problems where data is scarce, difficult to gather, or induces a prohibitive computational cost. As a first attempt, we focus on short-term elec…
Bayesian Uncertainty Quantification for Anaerobic Digestion models
Antoine Picard-Weibel, Gabriel Capson-Tojo, Benjamin Guedj +1
Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article intr…
FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
Sunrit Chakraborty, Saptarshi Roy, Debabrota Basu
High dimensional sparse linear bandits serve as an efficient model for sequential decision-making problems (e.g. personalized medicine), where high dimensional features (e.g. genom…
Free Proxies Unmasked: A Vulnerability and Longitudinal Analysis of Free Proxy Services
Naif Mehanna, Walter Rudametkin, Pierre Laperdrix +1
Free-proxies have been widespread since the early days of the Web, helping users bypass geo-blocked content and conceal their IP addresses. Various proxy providers promise faster I…
History-Aware and Dynamic Client Contribution in Federated Learning
Bishwamittra Ghosh, Debabrota Basu, Fu Huazhu +6
Federated Learning (FL) is a collaborative machine learning (ML) approach, where multiple clients participate in training an ML model without exposing their private data. Fair and…
Parallel Hyperparameter Optimization Of Spiking Neural Network
Thomas Firmin, Pierre Boulet, El-Ghazali Talbi
Spiking Neural Networks (SNN). SNNs are based on a more biologically inspired approach than usual artificial neural networks. Such models are characterized by complex dynamics betw…
Closed-form Filtering for Non-linear Systems
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj +1
Sequential Bayesian Filtering aims to estimate the current state distribution of a Hidden Markov Model, given the past observations. The problem is well-known to be intractable for…
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage
Achraf Azize, Debabrota Basu
In a Membership Inference (MI) game, an attacker tries to infer whether a target point was included or not in the input of an algorithm. Existing works show that some target points…
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories
Reabetswe M. Nkhumise, Debabrota Basu, Tony J. Prescott +1
The rising successes of RL are propelled by combining smart algorithmic strategies and deep architectures to optimize the distribution of returns and visitations over the state-act…
A Bandit Approach with Evolutionary Operators for Model Selection
Margaux Brégère, Julie Keisler
This work formulates model selection as an infinite-armed bandit problem, namely, a problem in which a decision maker iteratively selects one of an infinite number of fixed choices…
Reinforcement-learning robotic sailboats: simulator and preliminary results
Eduardo Charles Vasconcellos, Ronald M Sampaio, André P D Araújo +7
This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins.…
Comparative study of clustering models for multivariate time series from connected medical devices
Violaine Courrier, Christophe Biernacki, Cristian Preda +1
In healthcare, patient data is often collected as multivariate time series, providing a comprehensive view of a patient's health status over time. While this data can be sparse, co…