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

Submission Format
We welcome submissions up to 6 pages in JMLR Workshop and Proceedings format (plus 10 pages for references and appendix). All accepted papers will be presented as posters. We will invite the 2-3 best papers for an oral plenary presentation. Unless indicated by the authors, we will provide PDFs of all accepted papers on http://icml2021.automl.org/. There will be no archival proceedings. For submission details see https://sites.google.com/view/automl2021/submission.