Representative subset selection (RSS) is an important tool for users to draw insights from
massive datasets. Existing literature models RSS as the submodular maximization problem …

Active learning is an important machine learning setup for reducing the labelling effort of
humans. Although most existing works are based on a simple assumption that each …

We study pool-based active learning with abstention feedbacks where a labeler can abstain
from labeling a queried example with some unknown abstention rate. This is an important …

Constrained maximization of non-decreasing utility functions with submodularity-like
properties is at the core of several AI and ML applications including viral marketing, pool …

Submodular optimization has numerous applications such as crowdsourcing and viral
marketing. In this paper, we study the fundamental problem of non-negative submodular …

Active learning reduces the labeling cost by actively querying labels for the most valuable
data points. Typical active learning methods select the most informative examples one-at-a …

In recent years, data streams have become ubiquitous because many different applications,
eg, social media, communication networks, and Internet of things, are continuously …

We study pool-based active learning with abstention feedbacks, where a labeler can abstain
from labeling a queried example. We take a Bayesian approach to the problem and propose …