Toby Boyne — accepted AI-conference papers

2025

BARK: A Fully Bayesian Tree Kernel for Black-box Optimization

Toby Boyne, Jose Pablo Folch, Robert Matthew Lee, Behrang Shafei, Ruth Misener

ICML 2025poster

We perform Bayesian optimization using a Gaussian process perspective on Bayesian Additive Regression Trees (BART). Our BART Kernel (BARK) uses tree agreement to define a posterior over piecewise-constant functions, and we explore the space of tree kernels using a Markov chain Monte Carlo approach.…

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2025

The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning

Toby Boyne, Juan S Campos, Rebecca D. Langdon, Jixiang Qing, Yilin Xie, Shiqiang Zhang +8

NeurIPS 2025poster

Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemical datasets are often inaccessible to the machine learning community as they tend to require cleaning, thorough underst…

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