output
20042026
most citedConcentration around the mean for maxima of empirical processes

207 citations

122 papers

cs.LG2026

Energy-aware frugal Bayesian optimization

Gaston Plat, Paul Saves, Nathalie Bartoli +2

Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled…

cs.HC2026

FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

Peisen Xu, Jérémie Garcia, Peter Cleveland +2

As autonomous drone deployments scale from individual units to coordinated swarms, the human operator's role shifts from direct piloting to high-level supervision. Current interfac…

cs.RO2026

Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning

Timothée Gavin, Murat Bronz

This article presents a solution to intercept an agile drone by a team of agile drone carrying catching nets. We formulate the problem as a competitive Multi-Agent Reinforcement Le…

cs.AI2026

Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making

Pramudita Satria Palar, Paul Saves, Muhammad Daffa Robani +6

The simulation of complex systems increasingly relies on sophisticated but fundamentally opaque computational black-box simulators. Surrogate models play a central role in reducing…

math.OC2026

Safe and wind-aware synchronous path planning for a fleet of fixed-wing constant speed aircraft

Maël Feurgard, Gautier Hattenberger, Nicolas Durand +1

Path planning for multiple unmanned aerial vehicles is a difficult task, and even more for a fleet of fixed-wing aircraft. One specific case is the transition to, or between, forma…

cs.RO2026

Agile Interception of a Flying Target using Competitive Reinforcement Learning

Timothée Gavin, Simon Lacroix, Murat Bronz

This article presents a solution to intercept an agile drone by another agile drone carrying a catching net. We formulate the interception as a Competitive Reinforcement Learning p…