Abstract Domain incremental learning aims to adapt to a sequence of domains with access
to only a small subset of data (ie, memory) from previous domains. Various methods have …

Abstract Domain adaptation aims to mitigate distribution shifts among different domains.
However, traditional formulations are mostly limited to categorical domains, greatly …

Time series remains one of the most challenging modalities in machine learning research.
Out-of-distribution (OOD) detection and generalization on time series often face difficulties …

Invariance learning methods aim to learn invariant features in the hope that they generalize
under distributional shifts. Although many tasks are naturally characterized by continuous …

Recent studies on pre-trained vision/language models have demonstrated the practical
benefit of a new, promising solution-building paradigm in AI where models can be pre …

Existing methods, such as concept bottleneck models (CBMs), have been successful in
providing concept-based interpretations for black-box deep learning models. They typically …

Temporal Domain Generalization (TDG) addresses the challenge of training predictive
models under temporally varying data distributions. Traditional TDG approaches typically …

Recent methods are proposed to improve performance of domain adaptation by inferring
domain index under an adversarial variational bayesian framework, where domain index is …

Active learning (AL) aims to improve model performance within a fixed labeling budget by
choosing the most informative data points to label. Existing AL focuses on the single-domain …

Website fingerprinting (WF) attacks identify the websites visited over anonymized
connections by analyzing patterns in network traffic flows, such as packet sizes, directions …