Automated Machine Learning with Monte-Carlo Tree Search. - Herilalaina Rakotoarison; Marc Schoenauer; Michèle Sebag (IJCAI, 2019)

Automated Machine Learning with Monte-Carlo Tree Search (Extended Version). - Herilalaina Rakotoarison; Marc Schoenauer; Michèle Sebag (CoRR, 2019)

Distribution-Based Invariant Deep Networks for Learning Meta-Features. - Gwendoline de Bie; Herilalaina Rakotoarison; Gabriel Peyré; Michèle Sebag (CoRR, 2020)

Learning meta-features for AutoML. - Herilalaina Rakotoarison; Louisot Milijaona; Andry Rasoanaivo; Michèle Sebag; Marc Schoenauer (ICLR, 2022)

Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking. - Laurent Meunier; Herilalaina Rakotoarison; Pak-Kan Wong; Baptiste Rozière; Jérémy Rapin; Olivier Teytaud; Antoine Moreau; Carola Doerr (IEEE Trans. Evol. Comput., 2022)

Some contributions to AutoML: hyper-parameter optimization and meta-learning. (Contributions à AutoML: optimisation des hyper-paramètres et méta-apprentissage). - Herilalaina Rakotoarison (2022)

Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. - Steven Adriaensen; Herilalaina Rakotoarison; Samuel Müller 005; Frank Hutter (NeurIPS, 2023)

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. - Herilalaina Rakotoarison; Steven Adriaensen; Neeratyoy Mallik; Samir Garibov; Eddie Bergman; Frank Hutter (ICML, 2024)

In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization. - Herilalaina Rakotoarison; Steven Adriaensen; Neeratyoy Mallik; Samir Garibov; Edward Bergman; Frank Hutter (CoRR, 2024)

Warmstarting for Scaling Language Models. - Neeratyoy Mallik; Maciej Janowski; Johannes Hog; Herilalaina Rakotoarison; Aaron Klein; Josif Grabocka; Frank Hutter (CoRR, 2024)