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Automated Machine Learning with Monte-Carlo Tree Search
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The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand. Mosaic, a Monte-Carlo tree search (MCTS) based approach, is presented to handle the AutoML hybrid structural and parametric expensive black-box optimization problem. 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 OpenML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over Auto-Sklearn, winner of former international AutoML challenges.
Forward citations
Cited by 2 Pith papers
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ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning
ML-Master, a new AI4AI agent, achieves 29.3% medals on MLE-Bench by integrating MCTS-style exploration with reasoning steered by a compact adaptive memory, surpassing prior agents in half the time.
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VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale
VirnyFlow is a distributed ML pipeline optimizer that lets users define fairness, stability, and accuracy as weighted objectives and claims to outperform AutoML baselines.
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