Navigating Non-Convexity: Developing Efficient Algorithms for Complex Systems
Abstract: Efficient computational methods with proven guarantees are essential for navigating the complexities and nonlinearities of real-world systems. While practitioners often rely on heuristic algorithms tailored for specific applications, their theoretical foundations remain unclear, limiting their use in safety-critical systems. This presentation aims to bridge this gap for non-convex machine learning problems, emphasizing the critical need for robustness in machine learning algorithms, particularly in safety-critical applications such as autonomous driving and power systems.
The talk will delve into cutting-edge developments in provably robust machine learning, presenting novel models supported by mathematical proofs of their robustness. Additionally, it will introduce optimization techniques designed to certify existing models. These methods leverage underlying problem structures to achieve state-of-the-art robustness and efficiency, promising breakthroughs in both reliability and computational effectiveness.