The key idea behind active learning is that a machine learning algorithm can achieve
greater accuracy with fewer labeled training instances if it is allowed to choose the training …

Particulate matter (PM) is a crucial health risk factor for respiratory and cardiovascular
diseases. The smaller size fractions,≤ 2.5 μm (PM 2.5; fine particles) and≤ 0.1 μm (PM 0.1; …

Abstract Deep Gaussian processes (DGPs) are multi-layer generalizations of GPs, but
inference in these models has proved challenging. Existing approaches to inference in DGP …

Information theoretic active learning has been widely studied for probabilistic models. For
simple regression an optimal myopic policy is easily tractable. However, for other tasks and …

When monitoring spatial phenomena, which can often be modeled as Gaussian processes
(GPs), choosing sensor locations is a fundamental task. There are several common …

We consider the problem of choosing a set of k sensor measurements, from a set of m
possible or potential sensor measurements, that minimizes the error in estimating some …

Knowing how protein sequence maps to function (the “fitness landscape”) is critical for
understanding protein evolution as well as for engineering proteins with new and useful …

Recently active learning has attracted a lot of attention in computer vision field, as it is time
and cost consuming to prepare a good set of labeled images for vision data analysis. Most …

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 …

There has been much progress recently on improved approximations for problems involving
submodular objective functions, and many interesting techniques have been developed …