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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. T10-4Candidate approach

    Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

    Authors
    Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
    Year
    2018
    Venue
    Journal of Machine Learning Research 18 185, pp. 1-52

    Korean review of T10-4

  2. T10-5Candidate approach

    BOHB: Robust and Efficient Hyperparameter Optimization at Scale

    Authors
    Stefan Falkner, Aaron Klein, Frank Hutter
    Year
    2018
    Venue
    ICML 2018

    Korean review of T10-5

  3. T10-6Candidate approach

    Optuna: A Next-generation Hyperparameter Optimization Framework

    Authors
    Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama
    Year
    2019
    Venue
    KDD 2019 Applied Data Science Track

    Korean review of T10-6

  4. T10-15Candidate approach

    Model Cards for Model Reporting

    Authors
    Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru
    Year
    2019
    Venue
    ACM FAT* 2019

    Korean review of T10-15

  5. T10-16Candidate approach

    Datasheets for Datasets

    Authors
    Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daume III, Kate Crawford
    Year
    2021
    Venue
    Communications of the ACM vol. 64 no. 12 pp. 86-92

    Korean review of T10-16

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