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

15 12 月, 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.