Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

The ability to generalize well is one of the primary desiderata for models of natural language
processing (NLP), but what 'good generalization'entails and how it should be evaluated is …

Training state-of-the-art vision models has become prohibitively expensive for researchers
and practitioners. For the sake of accessibility and resource reuse, it is important to focus on …

Despite their renowned in-domain predictive power, convolutional neural networks are
known to rely more on high-frequency patterns that humans deem superficial than on low …

In this work, we connect two distinct concepts for unsupervised domain adaptation: feature
distribution alignment between domains by utilizing the task-specific decision boundary and …

Causal reasoning can shed new light on the major challenges in machine learning for
medical imaging: scarcity of high-quality annotated data and mismatch between the …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

In medicine, artificial intelligence (AI) research is becoming increasingly focused on
applying machine learning (ML) techniques to complex problems, and so allowing …