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  • AutoML | AutoML
    Other’s well-known AutoML packages include: AutoGluon is a multi-layer stacking approach of diverse ML models H2O AutoML provides automated model selection and ensembling for the H2O machine learning and data analytics platform MLBoX is an AutoML library with three components: preprocessing, optimisation and prediction
  • AutoML | Home
    AutoML is a major topic in the machine learning community and beyond To contribute to this field, the academic research groups at the University of Freiburg, led by Prof Frank Hutter, the Leibniz University of Hannover, led by Prof Marius Lindauer, and the University of Tübingen, led by Dr Katharina Eggensperger, develop new state-of-the-art approaches and open-source tools for topics
  • AutoML | Auto-Sklearn
    Auto-Sklearn Auto-sklearn provides out-of-the-box supervised machine learning Built around the scikit-learn machine learning library, auto-sklearn automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its hyperparameters
  • Tutorials and Invited Talks - AutoML
    The award was given for the KDD 2013 paper “Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms” by Chris Thornton, Frank Hutter, Holger Hoos and Kevin Leyton-Brown Slides
  • AutoML: Methods, Systems, Challenges (first book on AutoML)
    Chapter 4: Auto-WEKA [bibtex] By Lars Kotthoff and Chris Thornton and Holger H Hoos and Frank Hutter and Kevin Leyton-Brown Chapter 5: Hyperopt-Sklearn [bibtex] By Brent Komer and James Bergstra and Chris Eliasmith Chapter 6: Auto-sklearn: Efficient and Robust Automated Machine Learning [bibtex]
  • AutoML | Auto-PyTorch
    Auto-PyTorch While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search
  • ixAutoML: Interactive and Explainable AutoML
    ixAutoML: Interactive and Explainable AutoML Automating machine learning supports users, developers, and researchers in developing new ML applications fast The output of AutoML tools, however, cannot always be easily explained by human intuition or expert knowledge and thus experts sometimes lack trust in AutoML tools
  • AutoML | Automated Reinforcement Learning
    Reinforcement learning (RL) has shown impressive results in a variety of applications Well known examples include game and video game playing, robotics and, recently, “Autonomous navigation of stratospheric balloons” A lot of the successes came about by combining the expressiveness of deep learning with the power of RL Already on their own though, both frameworks … Continue reading
  • Hyperparameter Optimization - AutoML
    Hyperparameter Optimization The quality of performance of a Machine Learning model heavily depends on its hyperparameter settings Given a dataset and a task, the choice of the machine learning (ML) model and its hyperparameters is typically performed manually Hyperparameter Optimization (HPO) algorithms aim to alleviate this task as much as possible for the human expert The design of an HPO
  • Neural Architecture Search - AutoML
    Neural Ensemble Search for Uncertainty Estimation and Dataset Shift Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL





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