Accepted papers

Full papers:

  • Bayesian optimization for automated model selection [pdf]
    Luiz Gustavo Sant, Anna Malkomes Muniz, Chip Schaff and Roman Garnett

  • A Novel Bandit-Based Approach to Hyperparameter Optimization
    Lisha Li, Kevin Jamieson, Giulia Desalvo, Afshin Rostamizadeh and Ameet Talwalkar.

  • Scalable Structure Discovery in Regression using Gaussian Processes [pdf]
    Hyunjik Kim and Yee Whye Teh

  • Towards Automatically-Tuned Neural Networks [pdf]
    Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg and Frank Hutter

  • TPOT: A Tree-based Pipeline Optimization Tool for Automating Data Science [pdf]
    Randal Olson and Jason Moore

  • Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry [pdf]
    Manuel Martin Salvador, Marcin Budka and Bogdan Gabrys

  • Parameter-Free Convex Learning through Coin Betting [pdf]
    Francesco Orabona and David Pal

  • Effect of Incomplete Meta-dataset on Average Ranking Method [pdf]
    Salisu Abdulrahman and Pavel Brazdil

  • A Strategy for Ranking Optimization Methods using Multiple Criteria [pdf]
    Ian Dewancker, Michael Mccourt, Scott Clark, Patrick Hayes, Alexandra Johnson and George Ke
  • A Brief Review of the ChaLearn AutoML Challenge [pdf]
    Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Nuria Macia, Bisakha Ray, Lukasz Romaszko, Michele Sebag, Alexander Statnikov, Sebastien Treguer, Evelyne Viegas

AutoML Challenge System Description (extended summaries):

  • AutoML Challenge: System description of Lisheng Sun [pdf]
    Lisheng Sun

  • AutoML Challenge: AutoML Framework Using Random Space Partitioning Optimizer [pdf]
    Jungtaek Kim, Jongheon Jeong and Seungjin Choi

  • AutoML Challenge: Rules for Selecting Neural Network Architectures for AutoML-GPU Challenge [pdf]
    Abhishek Thakur