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Found 5 papers, 0 papers with code
Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings
Despite a large and significant body of recent work focused on estimating the out-of-sample risk of regularized models in the high dimensional regime, a theoretical understanding of this problem for non-differentiable penalties such as generalized LASSO and nuclear norm is missing.
Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers (extended version)
Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistically reliable estimate of LO (and OO) for generalized linear models with differentiable regularizers.
Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractability
Our method is fairly easy to implement and numerical experiments are presented to further demonstrate its practical merits.
On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errors
In this paper, we study the estimation of a rank-one spiked tensor in the presence of heavy tailed noise.
Perturbation Bounds for (Nearly) Orthogonally Decomposable Tensors
We develop deterministic perturbation bounds for singular values and vectors of orthogonally decomposable tensors, in a spirit similar to classical results for matrices such as those due to Weyl, Davis, Kahan and Wedin.
