Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

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 …

A common approach to transfer learning under distribution shift is to fine-tune the last few
layers of a pre-trained model, preserving learned features while also adapting to the new …

Deep classifiers are known to rely on spurious features—patterns which are correlated with
the target on the training data but not inherently relevant to the learning problem, such as the …

Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from
input data through a model. This problem has attracted increasing attention in the area of …

We study the problem of learning causal representations from unknown, latent interventions
in a general setting, where the latent distribution is Gaussian but the mixing function is …

Shortcut Learning of Large Language Models in Natural Language Understanding Page 1 110
COMMUNICATIONS OF THE ACM | JANUARY 2024 | VOL. 67 | NO. 1 research IMA GE B Y …

Machine learning models often perform poorly on subgroups that are underrepresented in
the training data. Yet, little is understood on the variation in mechanisms that cause …

We study neural network loss landscapes through the lens of mode connectivity, the
observation that minimizers of neural networks retrieved via training on a dataset are …

Recently, there has been a growing surge of interest in enabling machine learning systems
to generalize well to Out-of-Distribution (OOD) data. Most efforts are devoted to advancing …