Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many
diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular …

Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

Many recent private set intersection (PSI) protocols encode input sets as polynomials. We
consider the more general notion of an oblivious key-value store (OKVS), which is a data …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance
that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal …

To solve the approximate nearest neighbor search problem (NNS) on the sphere, we
propose a method using locality-sensitive filters (LSF), with the property that nearby vectors …

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose
distances to a query item are the smallest from a large database. Various methods have …

We show an optimal data-dependent hashing scheme for the approximate near neighbor
problem. For an n-point dataset in a d-dimensional space our data structure achieves query …

We propose a new decoding algorithm for random binary linear codes. The so-called
information set decoding algorithm of Prange (1962) achieves worst-case complexity 2 …

Locality-Sensitive Hashing (LSH) and its variants are the well-known indexing schemes for
the c-Approximate Nearest Neighbor (c-ANN) search problem in high-dimensional …