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cs.LG2026
PaMIR: Open Benchmark of Public Credit-Default Datasets
Mikhail Liashkov, Ilyas Varshavskiy, Shuhratjon Khalilbekov +2
We release PaMIR (Public Arrival-ordered Measurement for Inference in Risk), an open benchmark for credit-default prediction when labels are scarce and arrive late. The field's ref…
cs.LG2025
Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers
Ilyas Varshavskiy, Bonu Boboeva, Shuhrat Khalilbekov +4
Machine Learning models in finance are highly susceptible to model drift, where predictive performance declines as data distributions shift. This issue is especially acute in devel…
cs.LG2024
zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation
Azizjon Azimi, Bonu Boboeva, Ilyas Varshavskiy +4
The phenomenon of "black swans" has posed a fundamental challenge to performance of classical machine learning models. The perceived rise in frequency of outlier conditions, especi…