This program is tentative and subject to change.

Tue 22 Oct 2024 11:05 - 12:00 at Pacific C - NSAD: Session 1

Machine learning (ML) software is increasingly being deployed in high-stakes and sensitive applications, raising important challenges related to safety, privacy, and fairness. In response, ML verification has quickly gained traction within the formal methods community, particularly through techniques like abstract interpretation. However, much of this research has progressed with minimal dialogue and collaboration with the ML community, where it often goes underappreciated. In this talk, we advocate for closing this gap by surveying possible ways to make formal methods more appealing to the ML community. We will survey our recent and ongoing work in the design and development of abstract domains for machine learning verification, and discuss research questions and avenues for future work in this context.

This program is tentative and subject to change.

Tue 22 Oct

Displayed time zone: Pacific Time (US & Canada) change

11:00 - 12:30
NSAD: Session 1NSAD at Pacific C
11:00
5m
Opening
NSAD
Vincenzo Arceri University of Parma, Italy, Michele Pasqua University of Verona
11:05
55m
Keynote
Abstract Domains for Machine Learning Verification
NSAD
Caterina Urban Inria & École Normale Supérieure | Université PSL
12:00
30m
Full-paper
Towards a High Level Linter for Data ScienceFull Paper
NSAD
Greta Dolcetti Ca' Foscari University of Venice - Department of Environmental Sciences, Informatics and Statistics, Agostino Cortesi Università Ca' Foscari Venezia, Caterina Urban Inria & École Normale Supérieure | Université PSL, Enea Zaffanella University of Parma, Italy