AutoML 2016

9 papers

A Brief Review of the ChaLearn AutoML Challenge: Any-Time Any-Dataset Learning Without Human Intervention Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Núria Macià, Bisakha Ray, Lukasz Romaszko, Michèle Sebag, Alexander Statnikov, Sébastien Treguer, Evelyne Viegas

A Strategy for Ranking Optimization Methods Using Multiple Criteria Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson, George Ke

Adapting Multicomponent Predictive Systems Using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry Manuel Martin Salvador, Marcin Budka, Bogdan Gabrys

Bayesian Optimization for Automated Model Selection Gustavo Malkomes, Chip Schaff, Roman Garnett

Effect of Incomplete Meta-Dataset on Average Ranking Method Salisu Mamman Abdulrahman, Pavel Brazdil

Parameter-Free Convex Learning Through Coin Betting Francesco Orabona, Dávid Pál

Scalable Structure Discovery in Regression Using Gaussian Processes Hyunjik Kim, Yee Whye Teh

Towards Automatically-Tuned Neural Networks Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter

TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning Randal S. Olson, Jason H. Moore