Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables
spatially resolved imaging of optical tissue properties up to several centimeters deep in …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

The goal of domain generalization algorithms is to predict well on distributions different from
those seen during training. While a myriad of domain generalization algorithms exist …

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 …

In the domain generalization literature, a common objective is to learn representations
independent of the domain after conditioning on the class label. We show that this objective …

Federated Learning (FL) refers to the decentralized and privacy-preserving machine
learning framework in which multiple clients collaborate (with the help of a central server) to …

Graph neural networks (GNNs) have achieved impressive performance when testing and
training graph data come from identical distribution. However, existing GNNs lack out-of …

Despite the remarkable performance that modern deep neural networks have achieved on
independent and identically distributed (IID) data, they can crash under distribution shifts …

Despite remarkable success in a variety of applications, it is well-known that deep learning
can fail catastrophically when presented with out-of-distribution data. Toward addressing …