Publications

Publications are listed in reversed chronological order.

2025

  1. Moments Matter: Stabilizing Policy Optimization using Return Distributions
    Dennis Jabs*, Aditya Mohan*, and Marius Lindauer
    In 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2025), 2025

2024

  1. ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
    Jannis Becktepe*, Julian Dierkes*, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger Hoos, Marius Lindauer, and Theresa Eimer
    arXiv preprint arXiv:2409.18827, 2024
  2. Towards Enhancing Representations in Reinforcement Learning using Relational Structure
    Aditya Mohan, and Marius Lindauer
    In Seventeenth European Workshop on Reinforcement Learning, 2024
  3. Instance selection for dynamic algorithm configuration with reinforcement learning: Improving generalization
    Carolin Benjamins*, Gjorgjina Cenikj*, Ana Nikolikj, Aditya Mohan, Tome Eftimov, and Marius Lindauer
    In Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024
  4. Structure in Deep Reinforcement Learning: A survey and open problems
    Aditya Mohan, Amy Zhang, and Marius Lindauer
    Journal of Artificial Intelligence Research (JAIR), 2024

2023

  1. AutoRL Hyperparameter Landscapes
    Aditya Mohan*, Carolin Benjamins*, Konrad Wienecke, Alexander Dockhorn, and Marius Lindauer
    Proceedings of the Second International Conference on Automated Machine Learning (AutoML), 2023
  2. Contextualize Me - The Case for Context in Reinforcement Learning
    Carolin Benjamins*, Theresa Eimer*, F. Schubert, Aditya Mohan, Andre Biedenkapp, Bodo Rosenhahn, Frank Hutter, and Marius Lindauer
    Transactions on Machine Learning Research (TMLR), 2023
  3. Learning Activation Functions for Sparse Neural Networks
    Mohammad Loni*, Aditya Mohan*, Mehdi Asadi, and Marius Lindauer
    Proceedings of the Second International Conference on Automated Machine Learning (AutoML), 2023
  4. MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
    Tim Ruhkopf*, Aditya Mohan*, D. Deng, A. Tornede, F. Hutter, and Marius Lindauer
    Transactions on Machine Learning Research (TMLR), 2023
  5. AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
    Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, and Marius Lindauer
    arXiv preprint arXiv:2306.08107, 2023

2022

  1. Towards Meta-learned Algorithm Selection using Implicit Fidelity Information
    Aditya Mohan*, Tim Ruhkopf*, and Marius Lindauer
    ICML 2022 Workshop Adaptive Experimental Design and Active Learning in the Real World (ReALML), 2022