Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …

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 …

Learning exists in the context of data, yet notions of confidence typically focus on model
predictions, not label quality. Confident learning (CL) is an alternative approach which …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Tabular datasets are the last" unconquered castle" for deep learning, with traditional ML
methods like Gradient-Boosted Decision Trees still performing strongly even against recent …

While early AutoML frameworks focused on optimizing traditional ML pipelines and their
hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this …

We introduce a probability distribution, combined with an efficient sampling algorithm, for
weights and biases of fully-connected neural networks. In a supervised learning context, no …

Data valuation is a powerful framework for providing statistical insights into which data are
beneficial or detrimental to model training. Many Shapley-based data valuation methods …

Originally rooted in game theory, the Shapley Value (SV) has recently become an important
tool in machine learning research. Perhaps most notably, it is used for feature attribution and …

As the field of automated machine learning (AutoML) advances, it becomes increasingly
important to incorporate domain knowledge into these systems. We present an approach for …