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Found 7 papers, 2 papers with code
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier.
FiFAR: A Fraud Detection Dataset for Learning to Defer
Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI teaming.
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.
Lightweight Automated Feature Monitoring for Data Streams
We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications.
Active learning for imbalanced data under cold start
Modern systems that rely on Machine Learning (ML) for predictive modelling, may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain.
Automatic Model Monitoring for Data Streams
We evaluated SAMM using human feedback from domain experts, by sending them 100 reports generated by the system.
The N2HDM under Theoretical and Experimental Scrutiny
Its enlarged parameter space and its fewer symmetry conditions as compared to supersymmetric models allow for an interesting phenomenology compatible with current experimental constraints, while adding to the 2HDM sector the possibility of Higgs-to-Higgs decays with three different Higgs bosons.
High Energy Physics - Phenomenology
