Learning step-size adaptation in CMA-ES G Shala, A Biedenkapp, N Awad, S Adriaensen, M Lindauer, F Hutter Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference …, 2020 | 35 | 2020 |
Automated dynamic algorithm configuration S Adriaensen, A Biedenkapp, G Shala, N Awad, T Eimer, M Lindauer, ... Journal of Artificial Intelligence Research 75, 1633-1699, 2022 | 31 | 2022 |
Transfer NAS with meta-learned bayesian surrogates G Shala, T Elsken, F Hutter, J Grabocka The Eleventh International Conference on Learning Representations, 2023 | 12 | 2023 |
AutoRL-Bench 1.0 G Shala, SP Arango, A Biedenkapp, F Hutter, J Grabocka Sixth Workshop on Meta-Learning at the Conference on Neural Information …, 2022 | 5 | 2022 |
Squirrel: a switching hyperparameter optimizer N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ... arXiv preprint arXiv:2012.08180, 2020 | 5 | 2020 |
Gray-Box Gaussian Processes for Automated Reinforcement Learning G Shala, A Biedenkapp, F Hutter, J Grabocka The Eleventh International Conference on Learning Representations, 2023 | 2 | 2023 |
Hierarchical Transformers are Efficient Meta-Reinforcement Learners G Shala, A Biedenkapp, J Grabocka arXiv preprint arXiv:2402.06402, 2024 | | 2024 |
Method and device for learning a strategy and for implementing the strategy S Adriaenssen, A Biedenkapp, F Hutter, G Shala, M Lindauer, N Awad US Patent App. 17/305,586, 2022 | | 2022 |