The cost of annotating training data has traditionally been a bottleneck for supervised
learning approaches. The problem is further exacerbated when supervised learning is …

This paper addresses the problem of active learning on multiple tasks, where labeled data
are expensive to obtain for each individual task but the learning problems share some …

Learning of classification models from real-world data often requires substantial human
effort devoted to instance annotation. As this process can be very time-consuming and …

We study the problem of active learning for multi-class classification on large-scale datasets.
In this setting, the existing active learning approaches built upon uncertainty measures are …

In multi-task learning, the multiple related tasks allow each one to benefit from the learning
of the others, and labeling instances for one task can also affect the other tasks especially …

Multi-task learning is an emerging machine learning paradigm that integrates data from
multiple sources, harnessing task similarities to enhance overall model performance. The …

Training data acquisition for enabling supervised learning algorithms is an expensive
process. Current Active learning (AL) approaches to limit such costs by selectively acquiring …

Multitask learning (MTL) is a machine learning technique where a problem is learnt together
with other related problems at the same time, using a shared representation, so that …

Active learning may hold the key for solving the data scarcity problem in supervised
learning, ie, the lack of labeled data. Indeed, labeling data is a costly process, yet an active …

Learning of classification models in practice often relies on human annotation effort in which
humans assign class labels to data instances. As this process can be very time-consuming …