Partial label learning aims to induce a multi-class classifier from training examples where
each of them is associated with a set of candidate labels, among which only one is the …

We study active learning where the labeler can not only return incorrect labels but also
abstain from labeling. We consider different noise and abstention conditions of the labeler …

We consider the following natural generalization of Binary Search: in a given undirected,
positively weighted graph, one vertex is a target. The algorithm's task is to identify the target …

In image-based disease prediction, it can be hard to give certain cases a deterministic"
disease/normal" label due to lack of enough information, eg, at its early stage. We call such …

Data gathered in the real world normally contains noise, either stemming from inaccurate
experimental measurements or introduced by human errors. Our work deals with …

Active learning can improve the efficiency of training prediction models by identifying the
most informative new labels to acquire. However, non-response to label requests can impact …

Smart environments, such as smart cities and smart homes, are Cyber-Physical-Systems
(CPSs) which are becoming an increasing part of our everyday lives. Several applications in …

This paper considers a target localization problem where at any given time an agent can
choose a region to query for the presence of the target in that region. The measurement …

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 …

ABSTRACT With the Internet of Things paradigm, the data generated by the rapidly
increasing number of connected devices lead to new possibilities, such as using machine …