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AutoML 2020 : 7th ICML Workshop on Automated Machine Learning

Vienna, Austria
Event Date: Jul 17, 2020 - Jul 18, 2020
Submission Deadline: Apr 23, 2020
Notification of Acceptance: May 25, 2020

About

Machine learning has achieved considerable successes in recent years, but this success often relies on human experts, who construct appropriate features, design learning architectures, set their hyperparameters, and develop new learning algorithms. Driven by the demand for off-the-shelf machine learning methods from an ever-growing community, the research area of AutoML targets the progressive automation of machine learning aiming to make effective methods available to everyone. Hence, the workshop targets a broad audience ranging from core machine learning researchers in different fields of ML connected to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and learning to learn, to domain experts aiming to apply machine learning to new types of problems.

Call For Paper

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

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