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 …

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 …

Bio-inspired neuromorphic cameras asynchronously record visual information of dynamic
scenes by discrete events. Due to the high sampling rate, they are capable of fast motion …

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} …

This article proposes a hyperparameter optimization method for density-based spatial
clustering of applications with noise (DBSCAN) with constraints, termed HC-DBSCAN. While …

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 …

Many convex optimization problems with important applications in machine learning are
formulated as empirical risk minimization (ERM). There are several examples: linear and …

In this paper, we study the Dynamic Parameterized Subset Sampling (DPSS) problem in the
Word RAM model. In DPSS, the input is a set, S, of n items, where each item, x, has a non …