[KDD 2020] AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space
Aug 13, 20203 views
Data scientists seeking a good supervised learning model on a,dataset have many choices to make: they must preprocess the data,,select features, possibly reduce the dimension, select an estimation,algorithm, and choose hyperparameters for each of these pipeline,components. With new pipeline components comes a combinatorial explosion in the number of choices! In this work, we design,a new AutoML system,TensorOboe,to address this challenge: an,automated system to design a supervised learning pipeline.,TensorOboe,uses low rank tensor decomposition as a surrogate model,for efficient pipeline search. We also develop a new greedy experiment design protocol to gather information about a new dataset,efficiently. Experiments on large corpora of real-world classification,problems demonstrate the effectiveness of our approach.