Burglary Detection Framework for House Crime Control. - Nurbek Tastan; Abdul Razaque; Mohamed Ben Haj Frej; Amanzholova Saule Toksanovna; Raouf M. Ganda; Fathi H. Amsaad 001 (ICCSA (7), 2019)

CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation Loss. - Nurbek Tastan; Karthik Nandakumar (CVPR, 2023)

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline. - Anas Al-lahham; Muhammad Zaigham Zaheer; Nurbek Tastan; Karthik Nandakumar (CVPR, 2024)

Redefining Contributions: Shapley-Driven Federated Learning. - Nurbek Tastan; Samar Fares; Toluwani Aremu; Samuel Horváth; Karthik Nandakumar (IJCAI, 2024)

A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection. - Anas Al-Lahham; Nurbek Tastan; Muhammad Zaigham Zaheer; Karthik Nandakumar (WACV, 2024)

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning. - Nurbek Tastan; Samuel Horváth; Martin Takác 001; Karthik Nandakumar (CPAL, 2025)

CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning. - Nurbek Tastan; Samuel Horváth; Karthik Nandakumar (Trans. Mach. Learn. Res., 2025)

A Framework for Double-Blind Federated Adaptation of Foundation Models. - Nurbek Tastan; Karthik Nandakumar (CoRR, 2025)

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks. - Nurbek Tastan; Samuel Horváth; Karthik Nandakumar (CoRR, 2025)

LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning. - Nurbek Tastan; Stefanos Laskaridis; Martin Takác 001; Karthik Nandakumar; Samuel Horváth (CoRR, 2025)