Algorithms for Matrix Estimation
Many applications consists of data representable in the form of a sparse and dense matrix. The task of matrix estimation involves prediction from partially or fully observed (potentially noisy) matrices entries. The structural assumptions on the underlying matrix often lead to elegant algorithms and interpretations. This project involves novel algorithms for matrix estimation problems with strong statistical guarantees.