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| 09:00-10:00 |
Trustworthy Neuro-Symbolic Programming with Informal Specifications (abstract) 60 min
ABSTRACT. Neuro-Symbolic Programming (NSP) treats learning-enabled systems as programs that compose symbolic control structure composed with learned neural primitives for perception and prediction. NSP offers a principled response to a central challenge for logic and formal methods in the age of machine learning: how to obtain trustworthy, compositional behavior when key components are statistical and uncertain. This talk gives an gives an overview of recent advances in NSP along three axes: (1) domain-specific languages targeting real-world use cases, (2) learning techniques for fitting program structure based on informal specifications, and (3) approaches for ensuring correctness despite noisy neural components and incomplete specifications. |