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Invited & Accepted papers

Invited talk
  • Michele Sebag: Stochastic Gradient Descent: Going as Fast as Possible but not Faster
Accepted papers
  1. Martin Wistuba: 
       Bayesian Optimization Combined with Successive Halving for Neural Network Architecture Optimization
  2. Gust Verbruggen and Luc De Raedt:  
       Towards Automated Relational Data Wrangling 
  3. Fábio Pinto, Vitor Cerqueira, Carlos Soares and João Mendes-Moreira: 
       autoBagging: Learning to Rank Bagging Workflows with Metalearning    
  4. Gabriella Contardo, Ludovic Denoyer and Thierry Artieres: 
       A Meta-Learning Approach to One-Step Active-Learning
  5. Roxana Istrate, Cristiano Malossi, Costas Bekas and Dimitrios Nikolopoulos: 
       Incremental Training of Deep Convolutional Neural Networks
  6. Pieter Gijsbers, Joaquin Vanschoren and Randal Olson: 
       Layered TPOT: Speeding up Tree-based Pipeline Optimization.
  7. Miguel Cachada, Salisu Abdulrahman and Pavel Brazdil: 
       Combining Feature and Algorithm Hyperparameter Selection using some Metalearning Methods
  8. Silvia Nunes Das Dôres, Carlos Soares and Duncan Ruiz:  
       Effect of Metalearning on Feature Selection Employment
  9. Jan N. van Rijn and Frank Hutter: 
       An Empirical Study of Hyperparameter Importance Across Datasets
Late-breaking papers

       10. Sergey Muravyov and Andrey Filchenkov:
               Meta-learning system for automated clustering
      11. Alexey Zabashta and Andrey Filchenkov:
              NDSE: Instance Generation for Classification by Given Meta-Feature Description

CEUR Proceedings can be accessed at:
Pavel Brazdil,
Oct 17, 2017, 1:08 AM