Probabilistic Matrix Factorization for Automated Machine Learning

@inproceedings{Fusi2018ProbabilisticMF,
  title={Probabilistic Matrix Factorization for Automated Machine Learning},
  author={Nicol{\'o} Fusi and Rishit Sheth and Melih Elibol},
  booktitle={NeurIPS},
  year={2018}
}
In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuning of machine learning pipelines, which can include different data preprocessing methods and machine learning models, has long been one of the goals of the machine learning… CONTINUE READING

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