Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …

Self-training methods have gained significant attention in recent years due to their
effectiveness in leveraging small labeled datasets and large unlabeled observations for …

Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant
representations to narrow the domain shift, which are empirically effective but theoretically …

As larger deep learning models are hard to interpret, there has been a recent focus on
generating explanations of these black-box models. In contrast, we may have apriori …

Prior theoretical and empirical works have established that semi-supervised learning
algorithms can leverage the unlabeled data to improve over the labeled sample complexity …

Large neural networks trained in the overparameterized regime are able to fit noise to zero
train error. Recent work of Nakkiran and Bansal has empirically observed that such networks …

Semi-supervised learning is a popular machine learning paradigm that utilizes a large
amount of unlabeled data as well as a small amount of labeled data to facilitate learning …

Strong student models can learn from weaker teachers: when trained on the predictions of a
weaker model, a strong pretrained student can learn to correct the weak model's errors and …

We investigate the generalization properties of a self-training algorithm with halfspaces. The
approach learns a list of halfspaces iteratively from labeled and unlabeled training data, in …

In this paper, we propose a probabilistic framework for analyzing a multi-class majority vote
classifier in the case where training data is partially labeled. First, we derive a multi-class …