Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives
- Shaokun Zhang ,
- Feiran Jia ,
- Chi Wang ,
- Qingyun Wu
2023 International Conference on Learning Representations |
Motivated by various practical applications, we propose a novel and general formulation of targeted multi-objective hyperparameter optimization. Our formulation allows a clear specification of an automatable optimization goal using lexicographic preference over multiple objectives. We then propose a randomized directed search method named LexiFlow to solve this problem. We demonstrate the strong empirical performance of the proposed algorithm in multiple hyperparameter optimization tasks.
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FLAML: A Fast Library for AutoML and Tuning
December 15, 2020
FLAML is a Python library designed to automatically produce accurate machine learning models with low computational cost. It frees users from selecting learners and hyperparameters for each learner. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research.