The ability to train complex and highly effective models often requires an abundance of
training data, which can easily become a bottleneck in cost, time, and computational …

Causal discovery from observational and interventional data is challenging due to limited
data and non-identifiability which introduces uncertainties in estimating the underlying …

We consider the pool-based active learning problem, where only a subset of the training
data is labeled, and the goal is to query a batch of unlabeled samples to be labeled so as to …

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 …

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

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 …

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

We consider a batch active learning scenario where the learner adaptively issues batches of
points to a labeling oracle. Sampling labels in batches is highly desirable in practice due to …

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 …

We consider a batch active learning scenario where the learner adaptively issues batches of
points to a labeling oracle. Sampling labels in batches is highly desirable in practice due to …