6 citations · 8 across the 11 of their papers we have counts for
15 papers
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…
ArticleMiner: Ontology-Guided Knowledge Graph Construction from Scientific Publications
Md Abrar Jahin, Craig A. Knoblock, Jay Pujara
Scientific papers keep much of their quantitative content in tables and supplementary files, where a number means something only through its header, caption, unit, analytical metho…
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…
GUI-Primitives: Diagnosing Spatial Reasoning Failures in Vision-Language GUI Grounding
Md Abrar Jahin, Md Rizwan Parvez
Computer-use agents ground natural-language instructions in screenshots to locate interface elements, yet existing benchmarks do not isolate whether models bind relational language…
A Weisfeiler-Leman Characterization of Global-Attention Graph Transformers for Mixed-Integer Linear Programs
Md Abrar Jahin, Craig A. Knoblock, Jay Pujara
Graph foundation models (GFMs) with global attention are increasingly used to represent mixed-integer linear programs (MILPs), aiming to capture structure beyond the locality of st…
HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning
Md. Akmol Masud, Md Abrar Jahin, Mahmud Hasan
Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under non-IID data, these…
A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution
Md. Ariful Islam, Md Abrar Jahin, M. F. Mridha +1
Explainable Artificial Intelligence (XAI) methods are increasingly used in safety-critical domains, yet there is no unified framework to jointly evaluate fidelity, interpretability…
Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies
Abrar Morshed, Abdulla Al Shihab, Md Abrar Jahin +16
The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis a…
Designing Cellular Manufacturing Systems in the Presence of Alternative Process Plans
Md. Kutub Uddin, Md. Saiful Islam, Md Abrar Jahin +3
In the design of cellular manufacturing systems (CMS), numerous technological and managerial decisions must be made at both the design and operational stages. The first step in des…
Solving Generalized Grouping Problems in Cellular Manufacturing Systems Using a Network Flow Model
Md. Kutub Uddin, Md. Saiful Islam, Md Abrar Jahin +2
This paper focuses on the generalized grouping problem in the context of cellular manufacturing systems (CMS), where parts may have more than one process route. A process route lis…
Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network
Md Abrar Jahin, M. F. Mridha, Nilanjan Dey +1
Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interacti…
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
Md Abrar Jahin, Md. Akmol Masud, M. F. Mridha +2
In high-energy physics, particle jet tagging plays a pivotal role in distinguishing quark from gluon jets using data from collider experiments. While graph-based deep learning meth…
Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
Md Abrar Jahin, Md. Akmol Masud, Md Wahiduzzaman Suva +2
The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particl…