| Title: |
Locality-aware Surrogates for Gradient-based Black-box Optimization |
| Authors: |
Momeni, Ali, Uhlich, Stefan, Venkitaraman, Arun, Hsieh, Chia-Yu, Bonetti, Andrea, Matsuo, Ryoga, Ohbuchi, Eisaku, Servadei, Lorenzo |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Machine Learning |
| More Details: |
In physics and engineering, many processes are modeled using non-differentiable black-box simulators, making the optimization of such functions particularly challenging. To address such cases, inspired by the Gradient Theorem, we propose locality-aware surrogate models for active model-based black-box optimization. We first establish a theoretical connection between gradient alignment and the minimization of a Gradient Path Integral Equation (GradPIE) loss, which enforces consistency of the surrogate's gradients in local regions of the design space. Leveraging this theoretical insight, we develop a scalable training algorithm that minimizes the GradPIE loss, enabling both offline and online learning while maintaining computational efficiency. We evaluate our approach on three real-world tasks - spanning automated in silico experiments such as coupled nonlinear oscillators, analog circuits, and optical systems - and demonstrate consistent improvements in optimization efficiency under limited query budgets. Our results offer dependable solutions for both offline and online optimization tasks where reliable gradient estimation is needed. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2501.19161 |
| Accession Number: |
edsarx.2501.19161 |
| Database: |
arXiv |
