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 …

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} …

Deep learning has been widely used in many fields, but the model training process usually
consumes massive computational resources and time. Therefore, designing an efficient …

In modern machine learning, attention computation is a fundamental task for training large
language models such as Transformer, GPT-4 and ChatGPT. In this work, we study …

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 …

Given a matrix $ M\in\mathbb {R}^{m\times n} $, the low rank matrix completion problem
asks us to find a rank-$ k $ approximation of $ M $ as $ UV^\top $ for $ U\in\mathbb …

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 …