Deep Learning (DL) algorithms are the central focus of modern machine learning systems.
As data volumes keep growing, it has become customary to train large neural networks with …

Abstract Recently, Charikar and Siminelakis (2017) presented a framework for kernel
density estimation in provably sublinear query time, for kernels that possess a certain …

Nature disasters play a key role in sha** human-urban infrastructure interactions. Effective
and efficient response to natural disasters is essential for building resilience and sustainable …

In this paper we revisit the kernel density estimation problem: given a kernel K (x, y) and a
dataset of n points in high dimensional Euclidean space, prepare a data structure that can …

Kernel methods are effective but do not scale well to large scale data, especially in high
dimensions where the geometric data structures used to accelerate kernel evaluation suffer …

Abstract Markov Decision Process (MDP) with large action space naturally occurs in many
applications such as language processing, information retrieval, and recommendation …

We present the first provable Least-Squares Value Iteration (LSVI) algorithms that have
runtime complexity sublinear in the number of actions. We formulate the value function …

Entity resolution identifies and removes duplicate entities in large, noisy databases and has
grown in both usage and new developments as a result of increased data availability …

Learning representations in an unsupervised or self-supervised manner is a growing area of
research. Current approaches in representation learning seek to maximize the mutual …

Product search engines like Amazon Search often use caches to improve the customer user
experience; caches can improve both the system's latency as well as search quality …