Lunch and Learn with Kai Liu

January 24

10:30am - 11:30am

Engineering & Computer Science, Room 510, Event Space

Join us as Kai Liu speaks on Alternating Minimization in Machine Learning with Provable Convergence. Optimization is critical to a lot of machine learning methods such as Nonnegative Matrix Factorization, Dictionary Learning, and Principal Component Analysis. However, most existing algorithms can only guarantee that the objective function is monotonically non-increasing, while the convergence analysis of the generated sequences is usually ignored. In this talk, Kai Liu will introduce a new optimization framework, as well as its application in data recovery, denoising and mining with very promising results. He proved in theory that the new framework can ensure both the objective function and generated sequences are convergent with at least sub-linear convergence rate.