Tag Archives: ML

The Anatomy of a Misprediction: Tracking Terrestrial and Orbital Radiation-Induced Errors in Systolic Arrays

Rafael Tonetto, Pedro Pimenta, Abraham Chan, Karthik Pattabiraman, Fernando dos Santos, Marcello Traiola, Angeliki Kritikakou, Paolo Rech. To appear in the Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2026. (Acceptance Rate: 19.2%). [ PDF | Talk ]

Continue reading

The Statistical Assessment of Bayes-“sub”optimal Binary Machine Learning Classifier Risk

Abraham Chan, Ilir Gashi, Sathish Gopalakrishnan, Karthik Pattabiraman and Kizito Salako. To appear in the Proceedings of the International Conference on Computer Safety, Reliability and Security (Safecomp), 2026. (Acceptance Rate: TBD). [ PDF | Talk ]
Continue reading

Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices

Gargi Mitra, Mohammadreza Hallajiyan, Inji Kim, Athish Pranav Dharmalingam, Mohammed ElNawawy, Sharear Iqbal, Karthik Pattabiraman, Homa Alemzadeh. Springer Nature, 2026. (Invited) (arXIV version)
Continue reading

DLAFI: Software-Based Fault Injection for Permanent Faults in Deep Learning Accelerators

Seyedmani Sadati, Abraham Chan, Udit Kumar Agarwal and Karthik Pattabiraman. Proceedings of the IEEE International Symposium on Software Reliability Engineering (ISSRE) 2025. (Acceptance Rate: 28%) [ PDF | Talk ] (Code)
Continue reading

Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning Models

Zitao Chen and Karthik Pattabiraman, Proceedings of the ACM Conference on Computer and Communications Security (CCS), 2025. (Acceptance Rate: 14.5%) [ PDF | Talk ] (Code) Artifacts Available, Functional and Results Reproduced

Continue reading

ReMlX: Resilience for ML Ensembles using XAI at Inference against Faulty Training Data

Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan. Proceedings of the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2025. (Acceptance Rate: 20.1%) [ PDF | Talk ] (Code) Artifacts available, reviewed and reproducible.
Continue reading

D-semble: Efficient Diversity-Guided Search for Resilient ML Ensembles

Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan, Proceedings of the ACM International Symposium on Applied Computing (SAC), 2025. Safe, Secure, and Robust AI Track. (Acceptance Rate: 23%) [ PDF | Talk ] (code)
Continue reading

A Method to Facilitate Membership Inference Attacks in Deep Learning Models

Zitao Chen and Karthik Pattabiraman, Proceedings of the ISOC Network and Distributed Systems Security Symposium (NDSS), 2025. (Acceptance Rate: 16.1%) [ PDF | Talk ] (Code) (arXIV version). Artifacts Available, Functional and Results Reproduced.
Continue reading

Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models

Qutub Syed, Michael Paulitsch, Karthik Pattabiraman, Korbinian Hagn1, Fabian Oboril, Cornelius Buerkle, Kay-Ulrich Scholl, Gereon Hinz and Alois Knoll, Proceedings of the IJCAI-AISafety Workshop, 2024. [ PDF | Talk ]
Continue reading

Harnessing Explainability to Improve ML Ensemble Resilience

Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan, Supplementary proceedings of the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2024. Disrupt Track. (Acceptance Rate: TBD) [ PDF | Talk ]
Continue reading