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} …

We study the Kronecker product regression problem, in which the design matrix is a
Kronecker product of two or more matrices. Formally, given $ A_i\in\R^{n_i\times d_i} $ for …

In this work, we propose a sketching-based central path method for solving linear
programmings, whose running time matches the state of the art results [Cohen, Lee, Song …

Data-driven algorithms can adapt their internal structure or parameters to inputs from
unknown application-specific distributions, by learning from a training sample of inputs …

In this work, we propose a sketching-based central path method for solving linear
programmings, whose running time matches the state of art results [Cohen, Lee, Song STOC …

We study iterative methods based on Krylov subspaces for low-rank approximation under
any Schatten-p norm. Here, given access to a matrix A through matrix-vector products, an …

We create classical (non-quantum) dynamic data structures supporting queries for
recommender systems and least-squares regression that are comparable to their quantum …

We consider the problem of rank-1 low-rank approximation (LRA) in the matrix-vector
product model under various Schatten norms: _ ‖ u ‖ _ 2= 1\left ‖ A\left (Iu u …

The distance matrix of a dataset $ X $ of $ n $ points with respect to a distance function $ f $
represents all pairwise distances between points in $ X $ induced by $ f $. Due to their wide …

Semisupervised learning (SSL) has been widely used in numerous practical applications
where the labeled training examples are inadequate while the unlabeled examples are …