Nicholas Gisolfi — accepted AI-conference papers

2024

Data-Driven Discovery of Design Specifications (Student Abstract)

Angela Chen, Nicholas Gisolfi, Artur Dubrawski

AAAI 2024technical

Ensuring a machine learning model’s trustworthiness is crucial to prevent potential harm. One way to foster trust is through the formal verification of the model’s adherence to essential design requirements. However, this approach relies on well-defined, application-domain-centric criteria with whic…

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2023

Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract)

Prakruthi Pradeep, Benedikt Boecking, Nicholas Gisolfi, Jacob R. Kintz, Torin K. Clark, Artur Dubrawski

AAAI 2023technical

Crowdsourcing and weak supervision offer methods to efficiently label large datasets. Our work builds on existing weak supervision models to accommodate ordinal target classes, in an effort to recover ground truth from weak, external labels. We define a parameterized factor function and show that ou…

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