PABBO: Preferential Amortized Black-Box Optimization
Loading...
Access rights
openAccess
CC BY
CC BY
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
25
Series
13th International Conference on Learning Representations, ICLR 2025, pp. 98689-98713
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.Description
Publisher Copyright: © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Keywords
Other note
Citation
Zhang, X, Huang, D, Kaski, S & Martinelli, J 2025, PABBO: Preferential Amortized Black-Box Optimization. in 13th International Conference on Learning Representations, ICLR 2025. Curran Associates Inc., pp. 98689-98713, International Conference on Learning Representations, Singapore, Singapore, 24/04/2025. < https://openreview.net/forum?id=YhfrKB3Ah7 >