3 citations · 6 across the 4 of their papers we have counts for
5 papers
Stein Variational Black-Box Combinatorial Optimization
Thomas Landais, Olivier Goudet, Adrien Goëffon +2
Combinatorial black-box optimization in high-dimensional settings demands a careful trade-off between exploiting promising regions of the search space and preserving sufficient exp…
Reward-Preserving Attacks For Robust Reinforcement Learning
Lucas Schott, Elies Gherbi, Hatem Hajri +1
Adversarial training in reinforcement learning (RL) is challenging because perturbations cascade through trajectories and compound over time, making fixed-strength attacks either o…
Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learning
Olivier Goudet, Quentin Suire, Adrien Goëffon +2
We introduce an order-invariant reinforcement learning framework for black-box combinatorial optimization. Classical estimation-of-distribution algorithms (EDAs) often rely on lear…
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
Lucas Schott, Josephine Delas, Hatem Hajri +5
Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant…
Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
Jean Pinsolle, Olivier Goudet, Cyrille Enderli +2
In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exp…