Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Automated Machine Learning with Monte-Carlo Tree Search
Authors: Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical studies are conducted to independently assess and compare: i) the optimization processes based on Bayesian optimization or MCTS; ii) its warm-start initialization; iii) the ensembling of the solutions gathered along the search. MOSAIC is assessed on the Open ML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over AUTO-SKLEARN, winner of former international Auto ML challenges. |
| Researcher Affiliation | Academia | Herilalaina Rakotoarison , Marc Schoenauer and Mich ele Sebag TAU, LRI-CNRS INRIA Universit e Paris-Saclay, France |
| Pseudocode | Yes | Algorithm 1 MOSAIC Vanilla |
| Open Source Code | Yes | 1MOSAIC is publicly available under an open source license at https://github.com/herilalaina/mosaic_ml. |
| Open Datasets | Yes | The compared Auto ML systems are assessed on the Open ML repository [Vanschoren et al., 2013], including 100 classification problems. |
| Dataset Splits | Yes | For all systems, every considered x configuration is launched to learn a model from 70% of the training set with a cut-off time of 300 seconds, and performance F(x) is set to the model accuracy on the remaining 30%. |
| Hardware Specification | Yes | Computational times are measured on an AMD Athlon 64 X2, 5GB RAM. |
| Software Dependencies | No | The paper mentions using a "scikit-learn portfolio" and comparing against other systems like AUTO-SKLEARN and TPOT, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The overall computational budget is set to 1 hour for each dataset. ...MOSAIC involves 2 hyper-hyper-parameters...: the number ns = 100... Cucb = 1.3... PW = 0.6. Shared hyper-hyperparameters include: number nr of uniformly sampled configurations and variance ǫ = .2 for the local search in the Playout phase (Section 3.3). ...every considered x configuration is launched to learn a model from 70% of the training set with a cut-off time of 300 seconds, and performance F(x) is set to the model accuracy on the remaining 30%. |