Semantic communication is viewed as a revolutionary paradigm that can potentially
transform how we design and operate wireless communication systems. However, despite a …

The impact of automated decision-making systems on human lives is growing, emphasizing
the need for these systems to be not only accurate but also fair. The field of algorithmic …

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 …

Machine learning (ML) systems often exhibit unexpectedly poor behavior when they are
deployed in real-world domains. We identify underspecification in ML pipelines as a key …

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 …

The impressive performance of recent language models across a wide range of tasks
suggests that they possess a degree of abstract reasoning skills. Are these skills general …

A fundamental goal of scientific research is to learn about causal relationships. However,
despite its critical role in the life and social sciences, causality has not had the same …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Regularization is a fundamental technique to prevent over-fitting and to improve
generalization performances by constraining a model's complexity. Current Deep Networks …

As NLP models achieved state-of-the-art performances over benchmarks and gained wide
applications, it has been increasingly important to ensure the safe deployment of these …