Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization
- URL: http://arxiv.org/abs/2407.00610v1
- Date: Sun, 30 Jun 2024 06:58:31 GMT
- Title: Diff-BBO: Diffusion-Based Inverse Modeling for Black-Box Optimization
- Authors: Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu, Yi-An Ma, Rose Yu,
- Abstract summary: Black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle.
diffusion models have demonstrated impressive capability in learning the high-dimensional data manifold.
We propose Diff-BBO, the first inverse approach leveraging diffusion models for online BBO problem.
- Score: 20.45482366024264
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle. This process demands sample-efficient optimization due to the high computational cost of function evaluations. While prior studies focus on forward approaches to learn surrogates for the unknown objective function, they struggle with high-dimensional inputs where valid inputs form a small subspace (e.g., valid protein sequences), which is common in real-world tasks. Recently, diffusion models have demonstrated impressive capability in learning the high-dimensional data manifold. They have shown promising performance in black-box optimization tasks but only in offline settings. In this work, we propose diffusion-based inverse modeling for black-box optimization (Diff-BBO), the first inverse approach leveraging diffusion models for online BBO problem. Diff-BBO distinguishes itself from forward approaches through the design of acquisition function. Instead of proposing candidates in the design space, Diff-BBO employs a novel acquisition function Uncertainty-aware Exploration (UaE) to propose objective function values, which leverages the uncertainty of a conditional diffusion model to generate samples in the design space. Theoretically, we prove that using UaE leads to optimal optimization outcomes. Empirically, we redesign experiments on the Design-Bench benchmark for online settings and show that Diff-BBO achieves state-of-the-art performance.
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