ERC Starting Grant · 2024
Formalised Reasoning about Expectations: Composable, Automated, Speedy, Trustworthy
Automatic Differentiation (AD) systems, like TensorFlow, and probabilistic programming languages (PPLs), like Stan, automate complex computations of derivatives and Bayesian inference tasks. By stream- lining these computations for non-expert users, these high-level systems have accelerated progress across science and society (e.g. by enabling machine learning). Yet, the theoretical foundations needed to build a high-level system for composable programming with derivatives and probabilities are missing. This chasm in our knowledge severely limits the implementation of machine learning techniques, preventing them from reaching their full potential. Specifically, we do not understand (a) how…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.