Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …

H2O is an open source, distributed machine learning platform designed to scale to very
large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly …

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

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

With the continuous and vast increase in the amount of data in our digital world, it has been
acknowledged that the number of knowledgeable data scientists can not scale to address …

Comparing different AutoML frameworks is notoriously challenging and often done
incorrectly. We introduce an open and extensible benchmark that follows best practices and …

Matrices of (approximate) low rank are pervasive in data science, appearing in movie
preferences, text documents, survey data, medical records, and genomics. While there is a …

Given an unsupervised outlier detection task on a new dataset, how can we automatically
select a good outlier detection algorithm and its hyperparameter (s)(collectively called a …

The genetic analysis of complex traits has been dominated by parametric statistical methods
due to their theoretical properties, ease of use, computational efficiency, and intuitive …

Diffusion probabilistic models (DPMs) are a new class of generative models that have
achieved state-of-the-art generation quality in various domains. Despite the promise, one …