Convex and nonconvex optimization geometries
Many machine learning and signal processing problems are fundamentally nonconvex. One way to solve them is to transform them into convex optimization problems (a.k.a. convex relaxation), which constitutes a major part of my research. Although the convex relaxation approach is elegant in some ways that it can give information-theoretical sample convexity and minimax denoising rate, but this approach is not efficient in dealing with high-dimensional problems. Therefore, as my second major part of the research, I will directly focus on the fundamentally nonconvex formulations of these nonconvex ...
(For more, see "View full record.")