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

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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

Chattez avec Graph Search

Best of Both Worlds: Regret Minimization versus Minimax Play

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Learning Hand State Estimation for a Light Exoskeleton

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

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

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

Exploring FPGA Switch-Blocks without Explicitly Listing Connectivity Patterns

Source identification via contact tracing in the presence of asymptomatic patients

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

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