The distributional shift issue between training and test sets is a critical challenge in machine
learning, and is aggravated when models capture unintended decision rules with spurious …

Many machine learning algorithms are trained and evaluated by splitting data from a single
source into training and test sets. While such focus on in-distribution learning scenarios has …

Image classification models tend to make decisions based on peripheral attributes of data
items that have strong correlation with a target variable (ie, dataset bias). These biased …

Deep neural networks often make decisions based on the spurious correlations inherent in
the dataset, failing to generalize in an unbiased data distribution. Although previous …

Deep learning models are challenged by the distribution shift between the training data and
test data. Recently, the large models pre-trained on diverse data have demonstrated …

Approaches based on deep neural networks have achieved striking performance when
testing data and training data share similar distribution, but can significantly fail otherwise …

We are interested in learning data-driven representations that can generalize well, even
when trained on inherently biased data. In particular, we face the case where some …

Neural networks are prone to be biased towards spurious correlations between classes and
latent attributes exhibited in a major portion of training data, which ruins their generalization …

Despite the impressive prediction ability, machine learning models show discrimination
towards certain demographics and suffer from unfair prediction behaviors. To alleviate the …

Active learning (AL) aims to minimize labeling efforts for data-demanding deep neural
networks (DNNs) by selecting the most representative data points for annotation. However …