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 Article: fsews23sesafemlmain-id3372-p doi:10.1145/3617574.3622747

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 Article: fsews23sesafemlmain-id4515-p doi:10.1145/3617574.3617858

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 Article: fsews23sesafemlmain-id5149-p doi:10.1145/3617574.3617859

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 Article: fsews23sesafemlmain-id5493-p doi:10.1145/3617574.3617860

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

Publisher's Version Article: fsews23sesafemlmain-id7660-p doi:10.1145/3617574.3617861

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

Publisher's Version Article: fsews23sesafemlmain-id8716-p doi:10.1145/3617574.3617862

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