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Estimation of covariance matrices or their inverses plays a central role in many statistical methods. For these methods to work reliably, estimated matrices must not only be invertible but also well-c...
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tens...
Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of $l_1$ vector norm, the mixed $l_{2,1}$ ...
Aulbach et al. (2012) introduced some mathematical framework for extreme value theory in the space of continuous functions on compact intervals. Continuous max-stable processes on [0,1] were character...
This paper studies matrix completion under a general sampling model using the max-norm as a convex relaxation for the rank of the matrix. The optimal rate of convergence is established for the Frobeni...
In this paper, we propose three extensions of trace norm for the minimization of tensor rank via convex optimization. The alternating direction method of multipliers is used to efficiently solve the o...
Equivalent upper and lower bounds for the L1 norm of Hilbert space valued infinitely divisible random variables are obtained and used to find bounds for different types of stochastic integrals.

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