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T10. AutoML, model selection, evaluation design, and experiment tracking

Research on choosing and validating models instead of shipping a single fitted model.

  1. T10-1Similar problem

    Random Search for Hyper-Parameter Optimization

    Authors
    James Bergstra, Yoshua Bengio
    Year
    2012
    Venue
    Journal of Machine Learning Research vol. 13 pp. 281-305

    Korean review of T10-1

  2. T10-9Similar problem

    AMLB: an AutoML Benchmark

    Authors
    Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sebastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren
    Year
    2024
    Venue
    Journal of Machine Learning Research 25 101, pp. 1-65

    Korean review of T10-9

  3. T10-10Similar problem

    Statistical Comparisons of Classifiers over Multiple Data Sets

    Authors
    Janez Demsar
    Year
    2006
    Venue
    Journal of Machine Learning Research vol. 7 pp. 1-30

    Korean review of T10-10

  4. T10-11Similar problem

    On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

    Authors
    Gavin C. Cawley, Nicola L. C. Talbot
    Year
    2079
    Venue
    Journal of Machine Learning Research vol. 11 pp. 2079-2107

    Korean review of T10-11

  5. T10-12Similar problem

    Evaluating time series forecasting models: An empirical study on performance estimation methods

    Authors
    Vitor Cerqueira, Luis Torgo, Igor Mozetic
    Year
    2028
    Venue
    Machine Learning vol. 109 pp. 1997-2028

    Korean review of T10-12

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