Chen, Z., Foo, C.-S. &; Low, B.K.H.. (2024). Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:6699-6727
Abstract:
Machine learning (ML) models in the real world typically do not exist in isolation. They are usually part of a complex system (e.g., healthcare systems, self-driving cars) containing multiple ML and black-box components. The problem of optimizing such systems, which we refer to as automated AI (AutoAI), requires us to jointly train all ML components together and presents a significant challenge because the number of system parameters is extremely high and the system has no analytical form. To circumvent this, we introduce a novel algorithm called A-BAD-BO which uses each ML component’s local loss as an auxiliary indicator for system performance. A-BAD-BO uses Bayesian optimization (BO) to optimize the local loss configuration of a system in a smaller dimensional space and exploits the differentiable structure of ML components to recover optimal system parameters from the optimized configuration. We show A-BAD-BO converges to optimal system parameters by showing that it is asymptotically no regret. We use A-BAD-BO to optimize several synthetic and real-world complex systems, including a prompt engineering pipeline for large language models containing millions of system parameters. Our results demonstrate that A-BAD-BO yields better system optimality than gradient-driven baselines and is more sample-efficient than pure BO algorithms.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Research Foundation - CREATE Programme
Grant Reference no. : NA
This research / project is supported by the National Research Foundation and DSO National Laboratories - AI Singapore Programme
Grant Reference no. : AISG2-RP-2020-018