Publications associées à Aircraft Trajectory Planning for Climate Hotspot Avoidance Considering Air Traffic Complexity: A Constrained Multi-Agent Reinforcement Learning Approach

Publications associées (12)

Best of Both Worlds: Regret Minimization versus Minimax Play

In this paper, we investigate the existence of online learning algorithms with bandit feedback that simultaneously guarantee O(1) regret compared to a given comparator strategy, and Õ(√ T) regret compared to any fixed strategy, where T is the number of rou ...

2025

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Ali H. Sayed

To protect both local gradients and estimated parameters in distributed learning, this paper introduces a masked diffusion (MD) strategy, leading to two algorithms: the MD stochastic gradient (MD-SG) and the MD primal-dual stochastic gradient (MPD-SG). The ...

2025

Improved Gradients for Inverse Rendering

Baptiste Alexandre Marie Philippe Claude Nicolet

Physically based rendering is one of the cornerstones of computer graphics, and has evolved in the last decades into a mature technology, with applications ranging from hyperrealistic movies to product design. The recent efficiency of traditional algorithm ...

EPFL2025

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Amire Bendjeddou

In this thesis, we study how combinatorial structures and symmetries emerge from analytic properties of graph polynomials and neural networks. Graph polynomials encode combinatorial aspects of the underlying graph in their coefficients and are remarkably u ...

EPFL2025

Learning Hand State Estimation for a Light Exoskeleton

Luca Randazzo

We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information ...

Springer Nature2025

A survey on vehicle–drone cooperative delivery operations optimization: Models, methods, and future research directions

Zhenyu Yang

With the rise of technology and market demand, unmanned devices, particularly drones, are increasingly used in logistics due to their speed and cost-efficiency. However, the persistence of limiting factors such as battery life and payload capacity has rend ...

2025

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation c ...

2024

Logic Learning From Demonstrations for Multi-Step Manipulation Tasks in Dynamic Environments

Sylvain Calinon, Teng Xue, Amirreza Razmjoo Fard

Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Exploring FPGA Switch-Blocks without Explicitly Listing Connectivity Patterns

Paolo Ienne, Stefan Nikolic

Increased lower metal resistance makes physical aspects of Field-Programmable Gate Array (FPGA) switch-blocks more relevant than before. The need to navigate a design space where each individual switch can have significant impact on the FPGA's performance ...

2024

Source identification via contact tracing in the presence of asymptomatic patients

Miguel-Angel Sanchez Ndoye, Patrick Thiran, Gergely Odor

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algori ...

SPRINGERNATURE2023

Semi Bandit Dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees

Volkan Cevher, Luca Viano, Efstratios Panteleimon Skoulakis

In this work, we introduce a new variant of online gradient descent, which provably converges to Nash Equilibria and simultaneously attains sublinear regret for the class of congestion games in the semi-bandit feedback setting. Our proposed method admits c ...

2023

Stochastic optimization and Markov chain-based scenario generation for exploiting the underlying flexibilities of an active distribution network

Sidi-Rachid Cherkaoui

This paper proposes a scalable stochastic optimization model and a Markov chain-based scenario generation method to benefit from an active distribution network's (ADN's) flexibility. The optimization variables are the dispatch plan, such as the active and ...

2023