Online matrix vector multiplication is a fundamental step and bottleneck in many machine
learning algorithms. It is defined as follows: given a matrix at the pre-processing phase, at …

We study the tradeoff between the statistical error and communication cost of distributed
statistical estimation problems in high dimensions. In the distributed sparse Gaussian mean …

Sketching is one of the most fundamental tools in large-scale machine learning. It enables
runtime and memory saving via randomly compressing the original large problem into lower …

Projection maintenance is one of the core data structure tasks. Efficient data structures for
projection maintenance have led to recent breakthroughs in many convex programming …

We study the ℓ1-low rank approximation problem, where for a given nxd matrix A and
approximation factor α≤ 1, the goal is to output a rank-k matrix  for which‖ A-Â‖ 1≤ α …

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 …

This paper studies the Principal Component Analysis (PCA) problem in the distributed and
streaming models of computation. Given a matrix A∈ R m× n, a rank parameter k< rank (A) …

A rising trend in theoretical deep learning is to understand why deep learning works through
Neural Tangent Kernel (NTK)[jgh18], a kernel method that is equivalent to using gradient …

In this work, we initiate the study of\emph {Dynamic Tensor Product Regression}. One has
matrices $ A_1\in\mathbb {R}^{n_1\times d_1},\ldots, A_q\in\mathbb {R}^{n_q\times d_q} …