5 citations · 6 across the 6 of their papers we have counts for
6 papers
Fairness-Aware Low-Rank Representation Fine-Tuning
Parameswaran Kamalaruban, Mark Anderson, Stuart Burrell +3
Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of these adapted classifiers remain undere…
Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges
Parameswaran Kamalaruban, Yulu Pi, Stuart Burrell +4
Ensuring fairness in transaction fraud detection models is vital due to the potential harms and legal implications of biased decision-making. Despite extensive research on algorith…
Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
Piotr Skalski, David Sutton, Stuart Burrell +2
Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineere…
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
Iker Perez, Jason Wong, Piotr Skalski +4
Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in…
Distinguishing spin pumping from spin rectification in organic semiconductor-based lateral spin pumping device architectures
Piotr Skalski, Olga Zadvorna, Deepak Venkateshvaran +1
Over the last two decades organic spintronics has developed into a striving field with exciting reports of long spin diffusion lengths and spin relaxation times in organic semicond…
Attribution of Predictive Uncertainties in Classification Models
Iker Perez, Piotr Skalski, Alec Barns-Graham +2
Predictive uncertainties in classification tasks are often a consequence of model inadequacy or insufficient training data. In popular applications, such as image processing, we ar…