7 citations · 13 across the 9 of their papers we have counts for
9 papers
Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks
Gaurav Dhama
Fraud detection in payment networks relies on labels generated through heterogeneous and imperfect observation processes, yet existing approaches treat fraud as a homogeneous binar…
The Fundamental Limits of Fraud Detection in Card Payment Networks
Gaurav Dhama
Card payment fraud detection is usually framed as a supervised classification problem. Although this approach has generated practical progress, improvement has remained incremental…
Causal Label Recovery in Payment Networks
Gaurav Dhama
Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates: authorization (declined trans…
Entity Alignment For Knowledge Graphs: Progress, Challenges, and Empirical Studies
Deepak Chaurasiya, Anil Surisetty, Nitish Kumar +5
Entity Alignment (EA) identifies entities across databases that refer to the same entity. Knowledge graph-based embedding methods have recently dominated EA techniques. Such method…
Dynamic Temporal Reconciliation by Reinforcement learning
Himanshi Charotia, Abhishek Garg, Gaurav Dhama +1
Planning based on long and short term time series forecasts is a common practice across many industries. In this context, temporal aggregation and reconciliation techniques have be…
Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorization
Himanshi Charotia, Abhishek Garg, Gaurav Dhama +1
In the context of time series forecasting, it is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. How…
Application of Deep Reinforcement Learning to Payment Fraud
Siddharth Vimal, Kanishka Kayathwal, Hardik Wadhwa +1
The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given ri…
KARL-Trans-NER: Knowledge Aware Representation Learning for Named Entity Recognition using Transformers
Avi Chawla, Nidhi Mulay, Vikas Bishnoi +1
The inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA res…
A Comparative Study of Transformers on Word Sense Disambiguation
Avi Chawla, Nidhi Mulay, Vikas Bishnoi +2
Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this…