Federated learning (FL) is a machine learning paradigm where multiple client devices train
models collaboratively without data exchange. Data heterogeneity problem is naturally …

Genome search and/or classification typically involves finding the best-match database
(reference) genomes and has become increasingly challenging due to the growing number …

We study the problem of $\text {\emph {vector set search}} $ with $\text {\emph {vector set
queries}} $. This task is analogous to traditional near-neighbor search, with the exception …

Group Testing (GT) is about learning a (hidden) subset $ K $, of size $ k $, of some large
domain $ N $, of size $ n\gg k $, using a sequence of queries. A result of a query provides …

Abstract We study the Combinatorial Group Testing (CGT) problem in a novel game-
theoretic framework, with a solution concept of Adversarial Equilibrium (AE). In this new …

With the surging popularity of approximate near-neighbor search (ANNS), driven by
advances in neural representation learning, the ability to serve queries accompanied by a …

The Transformer architecture is widely deployed in many popular and impactful Large
Language Models. At its core is the attention mechanism for calculating correlations …

This work presents an adaptive group testing framework for the range-based high
dimensional near neighbor search problem. Our method efficiently marks each item in a …

The quantization-based approaches are the effective techniques for addressing the problem
of approximate nearest neighbor search. However, most of these methods typically employ a …

The study in group testing aims to develop strategies to identify a small set of defective items
among a large population using a few pooled tests. The established techniques have been …