Active Learning (AL) aims to reduce the labeling burden by interactively selecting the most
informative samples from a pool of unlabeled data. While there has been extensive research …

In many domains, effectively applying machine learning models requires a large number of
annotations and labelled data, which might not be available in advance. Acquiring …

Conducting experiments and gathering data for machine learning models is a complex and
expensive endeavor, particularly when confronted with limited information. Typically …

At its core, this thesis aims to enhance the practicality of deep learning by improving the
label and training efficiency of deep learning models. To this end, we investigate data subset …

Active learning is a machine learning paradigm that aims to improve the performance of a
model by strategically selecting and querying unlabeled data. One effective selection …

Solving partial differential equations (PDEs) is a fundamental problem in engineering and
science. While neural PDE solvers can be more efficient than established numerical solvers …

This study addresses the challenging problem of active view selection and uncertainty
quantification within the domain of Radiance Fields. Neural Radiance Fields (NeRF) have …

Batch active learning is a popular approach for efficiently training machine learning models
on large, initially unlabelled datasets by repeatedly acquiring labels for batches of data …

We introduce BatchGFN--a novel approach for pool-based active learning that uses
generative flow networks to sample sets of data points proportional to a batch reward. With …

Active learning enhances the performance of machine learning methods, particularly in semi-
supervised cases, by judiciously selecting a limited number of unlabeled data points for …