Diffusion Models for Black-Box Optimization

Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:17842-17857, 2023.

Abstract

The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations. Prior works consider forward approaches that learn surrogates to the black-box function and inverse approaches that directly map function values to corresponding points in the input domain of the black-box function. These approaches are limited by the quality of the offline dataset and the difficulty in learning one-to-many mappings in high dimensions, respectively. We propose Denoising Diffusion Optimization Models (DDOM), a new inverse approach for offline black-box optimization based on diffusion models. Given an offline dataset, DDOM learns a conditional generative model over the domain of the black-box function conditioned on the function values. We investigate several design choices in DDOM, such as reweighting the dataset to focus on high function values and the use of classifier-free guidance at test-time to enable generalization to function values that can even exceed the dataset maxima. Empirically, we conduct experiments on the Design-Bench benchmark (Trabucco et al., 2022) and show that DDOM achieves results competitive with state-of-the-art baselines.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-krishnamoorthy23a, title = {Diffusion Models for Black-Box Optimization}, author = {Krishnamoorthy, Siddarth and Mashkaria, Satvik Mehul and Grover, Aditya}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {17842--17857}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/krishnamoorthy23a/krishnamoorthy23a.pdf}, url = {https://proceedings.mlr.press/v202/krishnamoorthy23a.html}, abstract = {The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations. Prior works consider forward approaches that learn surrogates to the black-box function and inverse approaches that directly map function values to corresponding points in the input domain of the black-box function. These approaches are limited by the quality of the offline dataset and the difficulty in learning one-to-many mappings in high dimensions, respectively. We propose Denoising Diffusion Optimization Models (DDOM), a new inverse approach for offline black-box optimization based on diffusion models. Given an offline dataset, DDOM learns a conditional generative model over the domain of the black-box function conditioned on the function values. We investigate several design choices in DDOM, such as reweighting the dataset to focus on high function values and the use of classifier-free guidance at test-time to enable generalization to function values that can even exceed the dataset maxima. Empirically, we conduct experiments on the Design-Bench benchmark (Trabucco et al., 2022) and show that DDOM achieves results competitive with state-of-the-art baselines.} }
Endnote
%0 Conference Paper %T Diffusion Models for Black-Box Optimization %A Siddarth Krishnamoorthy %A Satvik Mehul Mashkaria %A Aditya Grover %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-krishnamoorthy23a %I PMLR %P 17842--17857 %U https://proceedings.mlr.press/v202/krishnamoorthy23a.html %V 202 %X The goal of offline black-box optimization (BBO) is to optimize an expensive black-box function using a fixed dataset of function evaluations. Prior works consider forward approaches that learn surrogates to the black-box function and inverse approaches that directly map function values to corresponding points in the input domain of the black-box function. These approaches are limited by the quality of the offline dataset and the difficulty in learning one-to-many mappings in high dimensions, respectively. We propose Denoising Diffusion Optimization Models (DDOM), a new inverse approach for offline black-box optimization based on diffusion models. Given an offline dataset, DDOM learns a conditional generative model over the domain of the black-box function conditioned on the function values. We investigate several design choices in DDOM, such as reweighting the dataset to focus on high function values and the use of classifier-free guidance at test-time to enable generalization to function values that can even exceed the dataset maxima. Empirically, we conduct experiments on the Design-Bench benchmark (Trabucco et al., 2022) and show that DDOM achieves results competitive with state-of-the-art baselines.
APA
Krishnamoorthy, S., Mashkaria, S.M. & Grover, A.. (2023). Diffusion Models for Black-Box Optimization. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:17842-17857 Available from https://proceedings.mlr.press/v202/krishnamoorthy23a.html.

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