AutoML 2016
9 papers
Parameter-Free Convex Learning through Coin Betting
Francesco Orabona, Dávid Pál
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning
Randal S. Olson, Jason H. Moore
Towards Automatically-Tuned Neural Networks
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Frank Hutter
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
Scalable Structure Discovery in Regression using Gaussian Processes
Hyunjik Kim, Yee Whye Teh
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
A Strategy for Ranking Optimization Methods using Multiple Criteria
Ian Dewancker, Michael McCourt, Scott Clark, Patrick Hayes, Alexandra Johnson
Effect of Incomplete Meta-dataset on Average Ranking Method
Salisu Mamman Abdulrahman, Pavel Brazdil