11 papers
Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
Youwei Zhong, Ben Merbaum, Timos Antonopoulos +4
With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in safety-critical and l…
Learning GR(1) Specifications from Traces
Sam Nicholas Kouteili, William Fishell, Mark Santolucito +1
Constrained specification mining enables the automatic discovery of desired properties from system traces. Generalized Reactivity of Rank 1, or GR(1), is a fragment of LTL with pol…
Learning Randomized Reductions
Ferhat Erata, Orr Paradise, Thanos Typaldos +4
Randomized self-reductions (RSRs) express using evaluated at random correlated points, enabling self-correcting programs, instance-hiding protocols, and applications in…
Report on the Designing Accountable Software Systems Workshop
Catherine Albiston, Travis Breaux, Kat Dearstyne +13
The Workshop on Designing Accountable Software Systems (DASS) was convened in November 2024 with support from the U.S. National Science Foundation to engage a wide range of current…
Learning How to Cube
Ferhat Erata, Sam Kouteili, Thanos Typaldos +4
Despite the effectiveness of Cube-and-Conquer (C&C) for solving challenging Boolean Satisfiability (SAT) problems, no prior work has shown that transformer-based models can learn e…
Mining Beyond the Bools: Learning Data Transformations and Temporal Specifications
Sam Nicholas Kouteili, William Fishell, Christian Scaff +2
Mining specifications from execution traces presents an automated way of capturing characteristic system behaviors. However, existing approaches are largely restricted to Boolean a…