5 papers
Stability and Discretization Error of State Space Model Neural Operators
Abderrahim Bendahi, Adrien Fradin, Johan Peralez +2
Neural operators have emerged as a powerful, discretization-invariant framework for solving partial differential equations (PDEs). Although established approaches like the Deep Ope…
Towards Scalable Persistence-Based Topological Optimization
Abderrahim Bendahi, Alexandre Duplessis, Arnaud Fickinger
Persistence-based topological optimization deforms a point cloud by minimizing objectives of the form , where $\mathrm{Dgm}(X…
Statistical Guarantees for Offline Domain Randomization
Arnaud Fickinger, Abderrahim Bendahi, Stuart Russell
Reinforcement-learning (RL) agents often struggle when deployed from simulation to the real-world. A dominant strategy for reducing the sim-to-real gap is domain randomization (DR)…
Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin +1
The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work,…
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives
Abderrahim Bendahi, Adrien Fradin, Matthieu Lerasle
We introduce Local Reinforcement-Based Selection of Auxiliary Objectives (LRSAO), a novel approach that selects auxiliary objectives using reinforcement learning (RL) to support th…