How to Assess Step-Size Adaptation Mechanisms in Randomised Search. - Nikolaus Hansen; Asma Atamna; Anne Auger (PPSN, 2014)

Benchmarking IPOP-CMA-ES-TPA and IPOP-CMA-ES-MSR on the BBOB Noiseless Testbed. - Asma Atamna (GECCO (Companion), 2015)

Analysis of Linear Convergence of a (1 + 1)-ES with Augmented Lagrangian Constraint Handling. - Asma Atamna; Anne Auger; Nikolaus Hansen (GECCO, 2016)

Augmented Lagrangian Constraint Handling for CMA-ES - Case of a Single Linear Constraint. - Asma Atamna; Anne Auger; Nikolaus Hansen (PPSN, 2016)

Linearly Convergent Evolution Strategies via Augmented Lagrangian Constraint Handling. - Asma Atamna; Anne Auger; Nikolaus Hansen (FOGA, 2017)

Analysis of Randomized Adaptive Algorithms for Black-Box Continuous Constrained Optimization. (Analyse d'algorithmes stochastiques adaptatifs pour l'optimisation numérique boîte-noire avec contraintes). - Asma Atamna (2017)

A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks. - Asma Atamna; Nataliya Sokolovska; Jean-Claude Crivello (IDA, 2020)

HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection. - Asma Atamna; Chloé Clavel (INTERSPEECH, 2020)

On invariance and linear convergence of evolution strategies with augmented Lagrangian constraint handling. - Asma Atamna; Anne Auger; Nikolaus Hansen (Theor. Comput. Sci., 2020)

Convex optimization with an interpolation-based projection and its application to deep learning. - Riad Akrour; Asma Atamna; Jan Peters 001 (Mach. Learn., 2021)

Benchmarking several strategies to update the penalty parameters in AL-CMA-ES on the bbob-constrained testbed. - Paul Dufossé; Asma Atamna (GECCO Companion, 2022)

ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation. - Abhijeet Pendyala; Justin Dettmer; Tobias Glasmachers; Asma Atamna (LOD (1), 2023)

Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering. - Abhijeet Pendyala; Asma Atamna; Tobias Glasmachers (ECML/PKDD (10), 2024)

Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments. - Tom Maus; Asma Atamna; Tobias Glasmachers (CoRR, 2025)

Cumulative Learning Rate Adaptation: Revisiting Path-Based Schedules for SGD and Adam. - Asma Atamna; Tom Maus; Fabian Kievelitz; Tobias Glasmachers (CoRR, 2025)