One more special issue dedicated to the mathematical foundations of AI

This special issue aims to bridge the gap between theoretical research in stochastic analysis and optimization and its practical implementation in machine learning, specifically focusing on generative modeling and reinforcement learning. We encourage submissions that integrate rigorous mathematical foundations with impactful applied findings. Preference will be given to manuscripts that validate fundamental theoretical results through complex, real-world problems. The editorial team includes distinguished scholars from Russia, China, and Greece: Academician A. Avetisyan, Professors: E. Burnaev, A. Sifaleras, and E. Tian. The journal is led by Editor-in-Chief Professor A.N. Karapetyants of SFedU.

Link to more information: https://link.springer.com/collections/abceecbifg .

Previously, a special issue on a related topic was launched in the journal Mathematical Methods in Applied Sciences (Springer Nature): https://sfedu.ru/press-center/news/79244 .