T. Maus, A. Atamna, T. Glasmachers. Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments. To appear in Conference on Machine Learning, Optimization, and Data Science (LOD), 2025
A. Pendyala, A. Atamna, T. Glasmachers. Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering. Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2024
A. Pendyala, J. Dettmer, T. Glasmachers, A. Atamna. ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation. Conference on Machine Learning, Optimization, and Data Science (LOD), 2023
A. Atamna, C. Clavel. HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection. INTERSPEECH, 2020
A. Atamna, N. Sokolovska, J.-C. Crivello. A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks. Symposium on Intelligent Data Analysis (IDA), 2020
A. Atamna, A. Auger, N. Hansen. Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling. Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA), 2017
A. Atamna, A. Auger, N. Hansen. Augmented Lagrangian Constraint Handling for CMA-ES—Case of a Single Linear Constraint. Parallel Problem Solving from Nature (PPSN), 2016
A. Atamna, A. Auger, N. Hansen. Analysis of Linear Convergence of a (1+1)-ES with Augmented Lagrangian Constraint Handling. Genetic and Evolutionary Computation Conference (GECCO), 2016. Best Paper Award nominee
N. Hansen, A. Atamna, A. Auger. How to Assess Step-Size Adaptive Mechanisms in Randomised Search. Parallel Problem Solving from Nature (PPSN), 2014
R. Akrour, A. Atamna, J. Peters. Convex Optimization with an Interpolation-based Projection and its Application to Deep Learning. Machine Learning (MACH), 2021
A. Atamna, A. Auger, N. Hansen. On Invariance and Linear Convergence of Evolution Strategies with Augmented Lagrangian Constraint Handling. Theoretical Computer Science (TCS), 2018
P. Dufossé, A. Atamna. Benchmarking Several Strategies to Update the Penalty Parameters in AL-CMA-ES on the BBOB-Constrained Testbed. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion, 2022
A. Atamna. Benchmarking IPOP-CMA-ES-TPA and IPOP-CMA-ES-MSR on the BBOB Noiseless Testbed. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion, 2015
P. Sampaio, N. Hansen, D. Brockhoff, A. Auger, A. Atamna. A Methodology for Building Scalable Test Problems for Continuous Constrained Optimization. PGMO Days, 2017
A. Atamna, T. Maus, F. Kievelitz, T. Glasmachers. Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam. 2025
A. Atamna, N. Sokolovska, J.-C. Crivello. SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network. 2019
A. Atamna. Analysis of Randomized Adaptive Algorithms for Black-Box Continuous Constrained Optimization (title in French: Analyse d'algorithmes stochastiques adaptatifs pour l'optimisation numérique boîte-noire avec contraintes). University of Paris-Saclay, France, 2017