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 …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

While deep learning has enabled tremendous progress on text and image datasets, its
superiority on tabular data is not clear. We contribute extensive benchmarks of standard and …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

We study the application of large language models to zero-shot and few-shot classification
of tabular data. We prompt the large language model with a serialization of the tabular data …

Tabular data is one of the most commonly used types of data in machine learning. Despite
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …

The mission of an intrusion detection system (IDS) is to monitor network activities and
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …

We present TabPFN, a trained Transformer that can do supervised classification for small
tabular datasets in less than a second, needs no hyperparameter tuning and is competitive …

Fine-tuning pretrained language models (LMs) without making any architectural changes
has become a norm for learning various language downstream tasks. However, for non …

Turnover intention is an employee's reported willingness to leave her organization within a
given period of time and is often used for studying actual employee turnover. Since …