Control Theoretic

Control theory is increasingly used to analyze and improve machine learning algorithms and systems, focusing on achieving better performance, stability, and theoretical understanding. Current research emphasizes applying control-theoretic frameworks to reinforcement learning, fine-tuning deep neural networks (including the use of state-space models), and analyzing the resilience of networked control systems. This interdisciplinary approach yields improved algorithms with performance guarantees and offers valuable insights into the underlying dynamics of complex systems, impacting fields like robotics, adaptive control, and AI.