- author = "Herilalaina Rakotoarison",
- title = "Some contributions to {AutoML} : hyper-parameter optimization and meta-learning",
- school = "Universite Paris-Saclay",
- year = "2022",
- address = "Paris, France",
- month = Jun,
- keywords = "genetic algorithms, genetic programming, TPOT, Artificial Intelligence, cs.AI, Machine Learning, cs.LG, Meta-learning, Hyper-parameter tuning, Model selection, AutoML, Meta-apprentissage, Optimisation d'hyper-parametre, Selection des modeles",
- number = "2022UPASG044",
- hal_id = "tel-03783610",
- hal_version = "v1",
-
URL = "
https://theses.hal.science/tel-03783610",
-
URL = "
https://theses.hal.science/tel-03783610v1/file/96048_RAKOTOARISON_2022_archivage.pdf",
-
URL = "https://www.inria.fr/en/rakotoarison-automation-ai-machine-learning",
- size = "160 pages",
- abstract = "This thesis proposes three main contributions to advance the state-of-the-art of AutoML approaches. They are divided into two research directions: optimization (first contribution) and meta-learning (second and third contributions). The first contribution is a hybrid optimization algorithm, dubbed Mosaic, leveraging Monte-Carlo Tree Search and Bayesian Optimization to address the selection of algorithms and the tuning of hyper-parameters, respectively. The empirical assessment of the proposed approach shows its merits compared to Auto-sklearn and TPOT AutoML systems on OpenML 100. The second contribution introduces a novel neural network architecture, termed Dida, to learn a good representation of datasets (i.e., metafeatures) from scratch while enforcing invariances w.r.t features and rows permutations. Two proof-of-concept tasks (patch classification and performance prediction tasks) are considered. The proposed approach yields superior empirical performance compared to Dataset2Vec and DSS on both tasks. The third contribution addresses the limitation of Dida on handling standard dataset benchmarks. The proposed approach, called Metabu, relies on hand-crafted meta-features. The novelty of Metabu is two-fold: i) defining an oracle topology of datasets based on top-performing hyper-parameters; ii) leveraging Optimal Transport approach to align a mapping of the handcrafted meta-features with the oracle topology. The empirical results suggest that Metabu metafeature outperforms the baseline hand-crafted meta-features on three different tasks (assessing meta-features based topology, recommending hyper-parameters w.r.t topology, and warmstarting optimization algorithms).",
-
notes = "In english
Supervisor: Marc SCHOENAUER and Michele SEBAG",
