Labeling a large set of data is expensive. Active learning aims to tackle this problem by
asking to annotate only the most informative data from the unlabeled set. We propose a …

In this study, we investigate the task of data pre-selection, which aims to select instances for
labeling from an unlabeled dataset through a single pass, thereby optimizing performance …

In game theory, a game refers to a model of interaction among rational decision-makers or
players, making choices with the goal of achieving their individual objectives. Understanding …

Error disagreement-based active learning (AL) selects the data that maximally update the
error of a classification hypothesis. However, poor human supervision (eg, few labels …

In this position paper, we study interactive learning for structured output spaces, with a focus
on active learning, in which labels are unknown and must be acquired, and on skeptical …

Active learning methods, like uncertainty sampling, combined with probabilistic prediction
techniques have achieved success in various problems like image classification and text …

Active learning solves machine learning problems where acquiring labels for the data is
costly. A representative subset helps active learning by selecting the most useful subset of a …

Automated captioning of photos is a mission that incorporates the difficulties of photo
analysis and text generation. One essential feature of captioning is the concept of attention …