Author

Paul Duckworth

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Preprint Oct 2026

Steered Not Stirred: Effective Reward Tilting using Surrogate Reward Mixing

Continuous-time generative models, from diffusion models, flow matching, and now flow maps, owe their practical value to high-fidelity generation and steerability. In many applications, a ground-truth latent ``oracle''reward $R^*$ is unavailable or expensive to evaluate, e.g., human judgments or experimental measuremen...

Alec Sargood, Olivia Gallup, Georg Maierhofer et al. · 0 citations

#machine learning Preprint Sep 2026

ALF: An Active Learning Framework for Scientific Discovery

Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to advance the field. Active learning (AL) offers promise wherever labelling requires expensive experiment, measurement, or simulation. Most e...

Shikha Surana, Alex Hawkins-Hooker, Olivia Gallup et al. · 0 citations

#machine learning Preprint Sep 2026

Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization

Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of candidates to experimentally validate. While Bayesian optimization (BO) is a natural fit for this setting, as it uses past evaluations to gui...

Donney Fan, Colin Doumont, Aleksandra Kalisz et al. · 3 citations