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OPTUS: Automated Machine Learning for Unsupervised Data

Automated Machine Learning (or AutoML) is the task of automatically discovering models for data. AutoML automates the resource-intensive process of model discovery and reduces time taken for this task. AutoML has largely focused on supervised/labelled data because labels can be effectively used for the model optimization process. In contrast, unsupervised machine learning problems, by definition, do not have labels (or are not allowed to use them), therefore applying standard AutoML techniques is a significant challenge for unsupervised problems.
In this talk, we will introduce OPTUS (Pipeline Optimization for Unsupervised Data), which is a system for unsupervised AutoML. It uses meta-learning for building an AutoML solution for unsupervised problems such as outlier detection and clustering.