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This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. Recent applications of BO over strings have been hindered by the need to map inputs into a smooth and unconstrained latent space. Learning this projection is computationally and data-intensive. Our approach instead builds a powerful Gaussian process surrogate model based on string kernels, naturally supporting variable length inputs, and performs efficient acquisition function maximization for spaces with syntactic constraints. Experiments demonstrate considerably improved optimization over existing approaches across a broad range of constraints, including the popular setting where syntax is governed by a context-free grammar.
Author Information
Henry Moss (Lancaster University)
David Leslie (Lancaster University and PROWLER.io)
Daniel Beck (University of Melbourne)
Javier González (Microsoft Research Cambridge)
Paul Rayson (Lancaster University)
Related Events (a corresponding poster, oral, or spotlight)
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2020 Poster: BOSS: Bayesian Optimization over String Spaces »
Wed Dec 9th 05:00 -- 07:00 PM Room Poster Session 3
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