JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data

TL;DR

JUMBO is a scalable multi-task Bayesian optimization method that effectively leverages large offline datasets using a dual Gaussian Process framework, improving optimization efficiency in real-world applications.

Contribution

We introduce JUMBO, a novel MBO algorithm combining a cold-GP and warm-GP to enhance scalability and inference quality with large offline data, supported by theoretical regret bounds.

Findings

Significant performance improvements over existing methods.

Effective handling of large offline datasets.

Successful application to hyper-parameter tuning and circuit design.

Abstract

The goal of Multi-task Bayesian Optimization (MBO) is to minimize the number of queries required to accurately optimize a target black-box function, given access to offline evaluations of other auxiliary functions. When offline datasets are large, the scalability of prior approaches comes at the expense of expressivity and inference quality. We propose JUMBO, an MBO algorithm that sidesteps these limitations by querying additional data based on a combination of acquisition signals derived from training two Gaussian Processes (GP): a cold-GP operating directly in the input domain and a warm-GP that operates in the feature space of a deep neural network pretrained using the offline data. Such a decomposition can dynamically control the reliability of information derived from the online and offline data and the use of pretrained neural networks permits scalability to large offline datasets. Theoretically, we derive regret bounds for JUMBO and show that it achieves no-regret under conditions analogous to GP-UCB (Srinivas et. al. 2010). Empirically, we demonstrate significant performance improvements over existing approaches on two real-world optimization problems: hyper-parameter optimization and automated circuit design.