However, existing methods for evaluating this robustness metric are either computationally expensive or not very accurate.

Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples.

For (3), we relax our proposed test using techniques from robust statistics and imprecise probabilities.

Despite extensive efforts to create fairer machine learning (ML) datasets, there remains a limited understanding of the practical aspects of dataset curation.

We consider two versions: sequential, where Bob observes Alice's cut

Current question-answering benchmarks predominantly focus on accuracy in realizable prediction tasks.