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cs.LG2025
Hybrid Data can Enhance the Utility of Synthetic Data for Training Anti-Money Laundering Models
Rachel Chung, Pratyush Nidhi Sharma, Mikko Siponen +2
Money laundering is a critical global issue for financial institutions. Automated Anti-money laundering (AML) models, like Graph Neural Networks (GNN), can be trained to identify i…
cs.LG2024
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning
Raphael Lafargue, Luke Smith, Franck Vermet +4
The predominant method for computing confidence intervals (CI) in few-shot learning (FSL) is based on sampling the tasks with replacement, i.e.\ allowing the same samples to appear…