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Formalised Reasoning about Expectations: Composable, Automated, Speedy, Trustworthy

Project description

Automatic differentiation systems in probabilistic programmes

Automatic differentiation (AD) systems like TensorFlow and probabilistic programming languages (PPLs) such as Stan simplify complex calculations in machine learning. However, their effectiveness is limited by a lack of theoretical foundations for composable programming, especially for applying AD to probabilistic choices and integrating Bayesian inference algorithms. The ERC-funded FoRECAST project aims to advance programming language theory and tools to enable efficient computations with derivatives and probabilities. The project will tackle real-world modelling challenges by collaborating with domain experts on case studies while establishing semantic foundations and algorithms for composable AD in probabilistic programs. It will develop a practical stochastic AD system that incorporates innovative gradient estimation techniques and a user-friendly PPL for composable Bayesian inference.

Objective

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 to perform AD on programs built using probabilistic choices and expected values or (b) how to compose (i.e. combine and integrate) Bayesian inference algorithms. FoRECAST addresses this chasm by developing programming language theory and tools for flexible, composable, and efficient calculations with derivatives and prob- abilities. WP 1 develops case studies in collaboration with domain experts, to ensure that FoRECAST creates theory and systems relevant to real-world, complex modelling problems. WP 2 develops the semantic foundations, algorithms, and formalised correctness proofs for composable AD of probabilistic programs. WP 3 builds a practical stochastic (i.e. probabilistic) AD system that synthesises these novel gradient estimation techniques. WP 4 establishes theoretical foundations to compose Bayesian inference algorithms in PPLs. WP 5 implements a user-friendly PPL that facilitates composable Bayesian inference, enabling more flexible modelling for a wider user base. By mathematically formalising, generalising, optimising, and implementing a next generation PPL, this project will lay a trustworthy foundation upon which probabilistic data analysis applications (e.g. reinforcement learning, proteomics modelling, and paleoclimate reconstructions) can rise to the next level.

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Programme(s)

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Topic(s)

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Call for proposal

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(opens in new window) ERC-2024-STG

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Host institution

UNIVERSITEIT UTRECHT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 500 000,00
Address
HEIDELBERGLAAN 8
3584 CS Utrecht
Netherlands

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Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 500 000,00

Beneficiaries (1)

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