6 accepted papers
Subset or core-set selection offers a data-efficient way for training deep learning models. One-shot subset selection poses additional challenges as subset selection is only performed once and full set data become unavailable after the selection. However, most existing methods tend to choose either…
Multi-label active learning is a crucial yet challenging area in contemporary machine learning, often complicated by a large and sparse label space. This challenge is further exacerbated in active learning scenarios where labeling resources are constrained. Drawing inspiration from existing mixture…
In active learning (AL), we focus on reducing the data annotation cost from the model training perspective. However, "testing'', which often refers to the model evaluation process of using empirical risk to estimate the intractable true generalization risk, also requires data annotations. The annota…
We propose to approach active learning (AL) from a novel perspective of discovering and then ranking potential support vectors by leveraging the key properties of the dual space of a sparse kernel max-margin predictor. We theoretically analyze the change of a hinge loss in the dual form and provide…
Active learning (AL) aims to sample the most informative data instances for labeling, which makes the model fitting data efficient while significantly reducing the annotation cost. However, most existing AL models make a strong assumption that the annotated data instances are always assigned correct…
Multi-label classification (MLC) allows complex dependencies among labels, making it more suitable to model many real-world problems. However, data annotation for training MLC models becomes much more labor-intensive due to the correlated (hence non-exclusive) labels and a potential large and sparse…