Filip Guzy

Modern analytical systems must process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called concept drift , and it may harm the quality of the used models. Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the continuous...

Generative Adversarial Networks (GANs) are among the most popular contemporary machine learning algorithms. Despite remarkable successes in their developments, existing GANs cannot offer the appropriate tools to monitor their performance in a continual learning scenario when data distribution changes. We propose a complete framework for monitoring...

Modern analytical systems must be ready to process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called concept drift, and it may harm the quality of the used models. Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the...

Streaming data analysis is currently a rapidly growing research direction. One of the serious problems hindering the data stream classification is the fact that during the exploitation of the model, its probabilistic characteristics may change. This phenomenon is called concept drift. Until today, multiple methods have been proposed to overcome the...