Being able to spot defective parts is a critical component in large-scale industrial
manufacturing. A particular challenge that we address in this work is the cold-start problem …

Recent success in deep learning has partially been driven by training increasingly
overparametrized networks on ever larger datasets. It is therefore natural to ask: how much …

Abstract Deep Metric Learning (DML) is arguably one of the most influential lines of research
for learning visual similarities with many proposed approaches every year. Although the field …

We study the problem of selecting a subset of big data to train a classifier while incurring
minimal performance loss. We show the connection of submodularity to the data likelihood …

A typical camera trap survey may produce millions of images that require slow, expensive
manual review. Consequently, critical conservation questions may be answered too slowly …

Abstract While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle
communication and traffic information exchange, VANET is also vulnerable to different …

In the data stream scenario, input arrives very rapidly and there is limited memory to store
the input. Algorithms have to work with one or few passes over the data, space less than …

The use of Bayesian methods in large-scale data settings is attractive because of the rich
hierarchical models, uncertainty quantification, and prior specification they provide …

Given a set F of n positive functions over a ground set X, we consider the problem of
computing x* that minimizes the expression∑ f∈ Ff (x), over x∈ X. A typical application is …

In this manuscript, we offer a gentle review of submodularity and supermodularity and their
properties. We offer a plethora of submodular definitions; a full description of a number of …