9 citations · 9 across the 5 of their papers we have counts for
7 papers · 1 filter
CERBO: Certified Recourse at a Requested Rate for Gradient-Boosted Trees
Farhan Shahriyar Hossain, Taufikur Rahman Fuad, Md. Zarif Bin Hasnat +2
Recourse-aware learning trains models so that people who receive an adverse decision can reverse it with an affordable change. For gradient-boosted decision trees (GBDTs), the exis…
Differentiable Koopman Operator for Contrastive Learning on Dynamic Graphs
Md Abrar Jahin, Taufikur Rahman Fuad, Md Rizwan Parvez
Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive methods encode temporal dependenc…
The Selection Rule Decides the Winner: A Pre-Registered Audit of Open-Set Graph Anomaly Detection
Farhan Shahriyar Hossain, Taufikur Rahman Fuad, Md Abrar Jahin +1
Open-set graph anomaly detection trains on a few labeled anomalies from one class and must also find anomaly classes that were never labeled. Published results share three conventi…
Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph Completion
Md Abrar Jahin, Taufikur Rahman Fuad, Jay Pujara +1
Uncertain knowledge graphs (UKGs) extend knowledge graphs by assigning each triple a continuous confidence score. Since most possible triples lack observed confidences, recent meth…
RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection
Taufikur Rahman Fuad, Md Abrar Jahin, Amir Hussain
Zero-shot graph anomaly detection seeks to deploy a detector trained on source graphs to unseen, unlabeled targets, yet domain shift can make source-derived notions of normality un…
ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs
Md Abrar Jahin, Taufikur Rahman Fuad, Jay Pujara +1
Dynamic graph representation learning requires capturing both structural relations and temporal evolution, yet existing approaches face a core trade-off: attention-based methods of…
AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing
Md Abrar Jahin, Taufikur Rahman Fuad, M. F. Mridha +2
Federated Learning (FL) faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environmen…