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 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
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