Towards Learning Universal Hyperparameter Optimizers with Transformers
This work addresses the problem of improving HPO efficiency for machine learning practitioners by enabling learning from varied hyperparameter sets, though it is incremental in extending Transformer-based approaches to HPO.
The paper tackles the limitation of existing meta-learning hyperparameter optimization (HPO) methods by introducing OptFormer, a text-based Transformer framework that learns from diverse tuning data, such as Google's Vizier database, to jointly predict policies and functions. It demonstrates that OptFormer can imitate at least 7 HPO algorithms and provides more accurate and better-calibrated predictions compared to Gaussian Processes.
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution. However, existing methods are restricted to learning from experiments sharing the same set of hyperparameters. In this paper, we introduce the OptFormer, the first text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction when trained on vast tuning data from the wild, such as Google's Vizier database, one of the world's largest HPO datasets. Our extensive experiments demonstrate that the OptFormer can simultaneously imitate at least 7 different HPO algorithms, which can be further improved via its function uncertainty estimates. Compared to a Gaussian Process, the OptFormer also learns a robust prior distribution for hyperparameter response functions, and can thereby provide more accurate and better calibrated predictions. This work paves the path to future extensions for training a Transformer-based model as a general HPO optimizer.