CALL FOR CONTRIBUTIONS The AutoML Workshop @ ICML 2014
Beijing, China, June 25/26, 2014 Web: <http://www.bayesianoptimization.org>http://icml2014.automl.org<http://www.bayesianoptimization.org> Email: icml2...@automl.org ---------------------------------------------------------------- Important Dates: - Submission deadline: Friday 25 April, 2014 - Notification of acceptance: Friday 16 May, 2014 ---------------------------------------------------------------- Workshop Overview: Machine learning has achieved considerable success, but this success crucially relies on human machine learning experts to select appropriate features, workflows, ML paradigms, algorithms, and algorithm hyperparameters. Because the complexity of these tasks is often beyond non-experts, the rapid growth of machine learning applications has created a demand for machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML. AutoML aims to automate many different stages of the machine learning process. Relevant topics include: - Model selection, hyper-parameter optimization, and model search - Representation learning and automatic feature extraction / construction - Reusable workflows and automatic generation of workflows - Meta learning and transfer learning - Automatic problem "ingestion" (from raw data and miscellaneous formats) - Feature coding/transformation to match requirements of different learning algorithms - Automatically detecting and handling skewed data and/or missing values - Automatic leakage detection - Matching problems to methods/algorithms (beyond regression and classification) - Automatic acquisition of new data (active learning, experimental design) - Automatic report writing (providing insight from the automatic data analysis) - User interfaces for AutoML (e.g., "Turbo Tax for Machine Learning") - Automatic inference and differentiation - Automatic selection of evaluation metrics - Automatic creation of appropriately sized and stratified train, validation, and test sets - Parameterless, robust algorithms - Automatic algorithm selection to satisfy time/space constraints at train- or run-time - Run-time wrappers to detect data shift and other causes of prediction failure We encourage contributions in any of these areas. We welcome 2-page short-form submissions and 6-page long-form submissions. Submissions should be formatted using JMLR Workshop and Proceedings format (an example LaTeX file is available on the workshop website icml2014.automl.org). We also encourage submissions of previously-published material that is closely related to the workshop topic (for presentation only). Confirmed invited speakers: - Dan Roth: Language designed for novice ML developers - Holger Hoos: Programming by Optimization - Yoshua Bengio: Representation learning - Jasper Snoek: Hyper-parameter optimization - Vikash Masingka: Probabilistic programming Advisory Committee: James Bergstra, Nando de Freitas, Roman Garnett, Matt Hoffman, Michael Osborne, Alice Zheng Organizers: Frank Hutter, Rich Caruana, Rémi Bardenet, Misha Bilenko, Isabelle Guyon, Balázs Kégl, and Hugo Larochelle
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