PABBO: Preferential Amortized Black-Box Optimization

• URL: http://arxiv.org/abs/2503.00924v1
• Date: Sun, 02 Mar 2025 14:57:24 GMT
• Title: PABBO: Preferential Amortized Black-Box Optimization
• Authors: Xinyu Zhang, Daolang Huang, Samuel Kaski, Julien Martinelli,
• Abstract summary: Preferential Bayesian Optimization (PBO) is a sample-efficient method to learn latent user utilities from preferential feedback over a pair of designs.<n>We propose to circumvent this issue by fully amortizing PBO, meta-learning both the surrogate and the acquisition function.<n>Our method is several orders of magnitude faster than the usual Gaussian process-based strategies and often outperforms them in accuracy.
• Score: 24.019185659134294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Preferential Bayesian Optimization (PBO) is a sample-efficient method to learn latent user utilities from preferential feedback over a pair of designs. It relies on a statistical surrogate model for the latent function, usually a Gaussian process, and an acquisition strategy to select the next candidate pair to get user feedback on. Due to the non-conjugacy of the associated likelihood, every PBO step requires a significant amount of computations with various approximate inference techniques. This computational overhead is incompatible with the way humans interact with computers, hindering the use of PBO in real-world cases. Building on the recent advances of amortized BO, we propose to circumvent this issue by fully amortizing PBO, meta-learning both the surrogate and the acquisition function. Our method comprises a novel transformer neural process architecture, trained using reinforcement learning and tailored auxiliary losses. On a benchmark composed of synthetic and real-world datasets, our method is several orders of magnitude faster than the usual Gaussian process-based strategies and often outperforms them in accuracy.

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