Trust-region methods based on Kullback-Leibler divergence are pervasivel...
In this paper, we study the problem of recovering a low-rank matrix from...
Empirical evidence suggests that for a variety of overparameterized nonl...
We study the asymmetric matrix factorization problem under a natural
non...
We study the robust recovery of a low-rank matrix from sparsely and gros...
Tensors are widely used to represent multiway arrays of data. The recove...
The nuclear norm and Schatten-p quasi-norm of a matrix are popular rank
...
Existing results for low-rank matrix recovery largely focus on quadratic...
This paper proposes a new variant of Frank-Wolfe (FW), called kFW. Stand...
Low rank matrix recovery problems appear widely in statistics, combinato...
This paper develops a new class of nonconvex regularizers for low-rank m...
In this paper, we show that the bundle method can be applied to solve
se...
The task of recovering a low-rank matrix from its noisy linear measureme...
This paper develops a new storage-optimal algorithm that provably solves...
We introduce a few variants on Frank-Wolfe style algorithms suitable for...
In this paper, we introduce a powerful technique, Leave-One-Out, to the
...