Neural network classifiers can largely rely on simple spurious features, such as
backgrounds, to make predictions. However, even in these cases, we show that they still …

Abstract Machine learning algorithms typically assume that training and test examples are
drawn from the same distribution. However, distribution shift is a common problem in real …

Deep neural networks often rely on spurious correlations to make predictions, which hinders
generalization beyond training environments. For instance, models that associate cats with …

Distribution shifts occur when the test distribution differs from the training distribution, and
can considerably degrade performance of machine learning models deployed in the real …

Overfitting describes the phenomenon where a highly predictive model on the training data
generalizes poorly to future observations. It is a common concern when applying machine …

AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making
the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its …

We introduce the problem of domain adaptation under Open Set Label Shift (OSLS), where
the label distribution can change arbitrarily and a new class may arrive during deployment …

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to
large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate …

Robustness to distribution shift has become a growing concern for text and image models as
they transition from research subjects to deployment in the real world. However, high-quality …

While deep learning models have shown remarkable performance in various tasks, they are
susceptible to learning non-generalizable _spurious features_ rather than the core features …