Automated Machine Learning with Monte-Carlo Tree Search H Rakotoarison, M Schoenauer, M Sebag International Joint Conference on Artificial Intelligence 2019, 2019 | 72 | 2019 |
Black-box optimization revisited: Improving algorithm selection wizards through massive benchmarking L Meunier, H Rakotoarison, PK Wong, B Roziere, J Rapin, O Teytaud, ... IEEE Transactions on Evolutionary Computation 26 (3), 490-500, 2021 | 45 | 2021 |
Efficient bayesian learning curve extrapolation using prior-data fitted networks S Adriaensen, H Rakotoarison, S Müller, F Hutter Thirty-seventh Annual Conference on Neural Information Processing Systems, 2023 | 23 | 2023 |
Learning meta-features for AutoML H Rakotoarison, L Milijaona, A Rasoanaivo, M Sebag, M Schoenauer International Conference on Learning Representations (ICLR) 2022, 2022 | 21 | 2022 |
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization H Rakotoarison, S Adriaensen, N Mallik, S Garibov, E Bergman, F Hutter International Conference on Machine Learning (ICML), 2024 | 8 | 2024 |
AutoML with Monte Carlo Tree Search H Rakotoarison, M Sebag AutoML@ICML 2018, 2018 | 8 | 2018 |
Distribution-based invariant deep networks for learning meta-features G De Bie, H Rakotoarison, G Peyré, M Sebag arXiv preprint arXiv:2006.13708, 2020 | 4 | 2020 |
Some contributions to AutoML: hyper-parameter optimization and meta-learning H Rakotoarison Université Paris-Saclay, 2022 | 1 | 2022 |
Warmstarting for Scaling Language Models N Mallik, M Janowski, J Hog, H Rakotoarison, A Klein, J Grabocka, ... arXiv preprint arXiv:2411.07340, 2024 | | 2024 |
Results from the Joint Nevergrad and IOHprofiler Open Optimization Competition T Bäck, P Bennet, J de Nobel, C Doerr, J Dreo, H Rakotoarison, J Rapin, ... | | 2021 |
From Epoch to Sample Size: Developing New Data-driven Priors for Learning Curve Prior-Fitted Networks TJ Viering, S Adriaensen, H Rakotoarison, F Hutter AutoML Conference 2024 (Workshop Track), 0 | | |