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Found 4 papers, 2 papers with code
Adversarial training for tabular data with attack propagation
Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to stop them.
FairGBM: Gradient Boosting with Fairness Constraints
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy.
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within.
ARMS: Automated rules management system for fraud detection
We propose ARMS, an automated rules management system that evaluates the contribution of individual rules and optimizes the set of active rules using heuristic search and a user-defined loss-function.
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