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Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
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OncoSynth slashes treatment effect estimation errors by up to 66% in oncology, transforming how synthetic data can inform precision medicine.
Training digital twins for decision-making can drastically improve policy ranking and reduce regret, even with limited model capacity.
Imagine evolving user preferences post-deployment without expensive retraining: this paper offers a way to infer how neural network outputs change with hyperparameters, building surrogate models that adapt to new settings.
