We invite submissions on the topics of:

• Model selection, hyper-parameter optimization, and model search
• Neural architecture search
• Meta-learning and transfer learning
• Bayesian optimization for AutoML
• Evolutionary algorithms for AutoML
• Multi-fidelity optimization
• Predictive models of performance
• Automatic feature extraction / construction
• Automatic data cleaning
• Automatic generation of workflows / workflow reuse
• Automatic problem "ingestion" (from raw data and miscellaneous formats)
• Automatic feature transformation to match algorithm requirements
• Automatic acquisition of new data (active learning, experimental design)
• Automatic report generation (providing insight on automatic data analysis)
• Automatic selection of evaluation metrics / validation procedures
• Automatic selection of algorithms under time/space/power constraints
• Automatic construction of fair and unbiased machine learning models
• Automation of semi-supervised and unsupervised machine learning
• Demos of existing AutoML systems
• Robustness of AutoML systems (w.r.t. Randomized algorithms, data, hardware etc.)
• Human-in-the-loop approaches for AutoML
• Learning to learn new algorithms and strategies
• Hyperparameter agnostic algorithms