SE4SafeML 2023 – Proceedings

1st International Workshop on Dependability and Trustworthiness of Safety-Critical Systems with Machine Learned Components (SE4SafeML 2023)

Frontmatter

Papers

Rule-Based Testing of Neural Networks
Muhammad Usman, Youcheng Sun, Divya Gopinath, and Corina S. Păsăreanu
(University of Texas, USA; University of Manchester, UK; KBR @ NASA Ames Research Center, USA; Carnegie Mellon University, USA)

Publisher's Version

FedDefender: Backdoor Attack Defense in Federated Learning
Waris Gill, Ali Anwar, and Muhammad Ali Gulzar
(Virginia Tech, USA; University of Minnesota, USA)

Publisher's Version

MLGuard: Defend Your Machine Learning Model!
Sheng Wong, Scott Barnett, Jessica Rivera-Villicana, Anj Simmons, Hala Abdelkader, Jean-Guy Schneider, and Rajesh Vasa
(Deakin University, Australia; RMIT University, Australia; Monash University, Australia)

Publisher's Version

Interpretable On-the-Fly Repair of Deep Neural Classifiers
Hossein Mohasel Arjomandi and Reyhaneh Jabbarvand
(University of Illinois at Urbana-Champaign, USA)

Publisher's Version

Towards Safe ML-Based Systems in Presence of Feedback Loops
Sumon Biswas, Yining She, and Eunsuk Kang
(Carnegie Mellon University, USA)

Publisher's Version

The Case for Scalable Quantitative Neural Network Analysis
Mara Downing and Tevfik Bultan
(University of California at Santa Barbara, USA)

Publisher's Version

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