Continuous optimization plays a central role in modern machine learning. Every learning process requires solving a high-dimensional minimization problem. The faster we solve it, the fewer resources (e.g., energy, time, or money) we waste.
Current state-of-the-art algorithms include some heuristics and often require a lot of tuning. The theoretical justification of these heuristics and the developing of new, more efficient algorithms is the main focus of our group.
Our group focuses on developing more efficient algorithms for machine learning.
