no code implementations • 28 May 2024 • Arnab Auddy, Dong Xia, Ming Yuan
Large amount of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing.
no code implementations • 13 Feb 2024 • Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad, Arian Maleki
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.
no code implementations • 26 Oct 2023 • Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad, Arian Maleki
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.
no code implementations • 31 Mar 2023 • Arnab Auddy, Ming Yuan
Our method is fairly easy to implement and numerical experiments are presented to further demonstrate its practical merits.
no code implementations • 20 Jul 2021 • Arnab Auddy, Ming Yuan
In this paper, we study the estimation of a rank-one spiked tensor in the presence of heavy tailed noise.
no code implementations • 17 Jul 2020 • Arnab Auddy, Ming Yuan
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.