Predicting from Strings: Language Model Embeddings for Bayesian Optimization

• URL: http://arxiv.org/abs/2410.10190v2
• Date: Tue, 15 Oct 2024 17:23:08 GMT
• Title: Predicting from Strings: Language Model Embeddings for Bayesian Optimization
• Authors: Tung Nguyen, Qiuyi Zhang, Bangding Yang, Chansoo Lee, Jorg Bornschein, Yingjie Miao, Sagi Perel, Yutian Chen, Xingyou Song,
• Abstract summary: We propose Embed-then-Regress, a paradigm for applying in-context regression over string inputs. By expressing all inputs as strings we are able to perform general-purpose regression for Optimization over various domains.
• Score: 21.370382766970877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian Optimization is ubiquitous in the field of experimental design and blackbox optimization for improving search efficiency, but has been traditionally restricted to regression models which are only applicable to fixed search spaces and tabular input features. We propose Embed-then-Regress, a paradigm for applying in-context regression over string inputs, through the use of string embedding capabilities of pretrained language models. By expressing all inputs as strings, we are able to perform general-purpose regression for Bayesian Optimization over various domains including synthetic, combinatorial, and hyperparameter optimization, obtaining comparable results to state-of-the-art Gaussian Process-based algorithms. Code can be found at https://github.com/google-research/optformer/tree/main/optformer/embed_then_regress.

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