2 papers
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…