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Predicting from Strings: Language Model Embeddings for Bayesian Optimization

14 October 2024
Tung Nguyen
Qiuyi Zhang
Bangding Yang
Chansoo Lee
J. Bornschein
Yingjie Miao
Sagi Perel
Yutian Chen
Xingyou Song
    BDL
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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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