Influence functions efficiently estimate the effect of removing a single training data point on a
model's learned parameters. While influence estimates align well with leave-one-out …

Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious
correlations and poor generalization on minority groups. The recent deep feature …

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 …

Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse
downstream tasks. However, several pain points persist for this paradigm:(i) directly tuning …

Leakage of data from publicly available Machine Learning (ML) models is an area of
growing significance since commercial and government applications of ML can draw on …

Although powerful graph neural networks (GNNs) have boosted numerous real-world
applications, the potential privacy risk is still underexplored. To close this gap, we perform …

Deep neural networks (DNNs) have achieved high accuracy in diagnosing multiple
diseases/conditions at a large scale. However, a number of concerns have been raised …

Deep learning models can be vulnerable to recovery attacks, raising privacy concerns to
users, and widespread algorithms such as empirical risk minimization (ERM) often do not …

Neural networks often learn simple explanations that fit the majority of the data while
memorizing exceptions that deviate from these explanations. This behavior leads to poor …

Introduction With the increasing utilization of text-based suicide crisis counseling, new
means of identifying at risk clients must be explored. Natural language processing (NLP) …