38 citations · 46 across the 5 of their papers we have counts for
4 papers · 1 filter
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy
Sherin Mary Mathews, Samuel A. Assefa
Federated learning holds great promise in learning from fragmented sensitive data and has revolutionized how machine learning models are trained. This article provides a systematic…
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources
Parisa Hassanzadeh, Danial Dervovic, Samuel Assefa +2
Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures. These alerts often need…
SURF: Improving classifiers in production by learning from busy and noisy end users
Joshua Lockhart, Samuel Assefa, Ayham Alajdad +3
Supervised learning classifiers inevitably make mistakes in production, perhaps mis-labeling an email, or flagging an otherwise routine transaction as fraudulent. It is vital that…
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users
Joshua Lockhart, Samuel Assefa, Tucker Balch +1
Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it.…