Out-of-distribution (OOD) generalization is critical for machine learning models deployed in
the real world. However, achieving this can be fundamentally challenging, as it requires the …

Analyzing model performance in various unseen environments is a critical research problem
in the machine learning community. To study this problem, it is important to construct a …

The maximum entropy encoding framework provides a unified perspective for many non-
contrastive learning methods like SimSiam, Barlow Twins, and MEC. Inspired by this …

Modern machine learning models deployed often encounter distribution shifts in real-world
applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These …

Federated learning achieves effective performance in modeling decentralized data. In
practice client data are not well-labeled which makes it potential for federated unsupervised …

Self-supervised learning (SSL) is a popular paradigm for representation learning. Recent
multiview methods can be classified as sample-contrastive, dimension-contrastive, or …

Abstract Class Incremental Learning (CIL) constitutes a pivotal subfield within continual
learning, aimed at enabling models to progressively learn new classification tasks while …

Abstract Domain generalization learns from one or multiple source domains. It aims to
extract a domain-invariant model that can be employed in an unknown target domain …

Uncertainty quantification in Large Language Models (LLMs) is crucial for applications
where safety and reliability are important. In particular, uncertainty can be used to improve …

Empirical data can often be considered as samples from a set of probability distributions.
Kernel methods have emerged as a natural approach for learning to classify these …