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.

本文介绍了 OptFormer，它是第一个基于文本的 Transformer HPO 框架，可以在从 Google 的 Vizier 数据库等多种调整数据中训练，以提供学习策略和功能预测的通用端到端接口。OptFormer 能同时模拟至少 7 种不同的 HPO 算法，可以通过其函数不确定性估计进一步改进，并学习到对超参数响应函数的强健先验分布，可以提供更准确和更好的校准预测，这项工作为训练基于 Transformer 模型作为通用 HPO 优化器的未来扩展铺平了道路。