Tensor Attention, a multi-view attention that is able to capture high-order correlations among
multiple modalities, can overcome the representational limitations of classical matrix …

We consider the problem of estimating the mean and covariance of a distribution from iid
samples in the presence of a fraction of malicious noise. This is in contrast to much recent …

We develop efficient algorithms for estimating low-degree moments of unknown distributions
in the presence of adversarial outliers and design a new family of convex relaxations for k …

We give a new approach to the dictionary learning (also known as" sparse coding") problem
of recovering an unknown nxm matrix A (for m≥ n) from examples of the form y= Ax+ e …

We consider relative error low rank approximation of tensors with respect to the Frobenius
norm. Namely, given an order-q tensor A∊ ℝ∏ i= 1 q ni, output a rank-k tensor B for which …

Low rank decomposition of tensors is a powerful tool for learning generative models. The
uniqueness results that hold for tensors give them a significant advantage over matrices …

Understanding when and how much a model gradient leaks information about the training
sample is an important question in privacy. In this paper, we present a surprising result …

We consider the problem of efficiently learning mixtures of a large number of spherical
Gaussians, when the components of the mixture are well separated. In the most basic form …

In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework
is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) …

We propose a new sufficient condition for verifying whether general rank-r complex tensors
of arbitrary order admit a unique decomposition as a linear combination of rank-1 tensors. A …