The gradient boosting machine (GBM) is one of the most successful supervised learning algorithms, and it has been the dominant method in many data science competitions, including Kaggle and KDDCup. In spite of its practical success, there has been a huge gap between practice and theoretical understanding. In this line of research, we show that GBM can be interpreted as a greedy coordinate descent method in the coefficient space and/or a mirror descent method in the “pseudo-residual” space.
Convex optimization is the cornerstone of continuous optimization, but many real problems are nonconvex: deep learning, optimizing drinking water networks, etc. This two part talk explores my work developing efficient algorithms for finding local minima of nonconvex functions.