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AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Charles Weill
Vitaly Kuznetsov
Scott Yang
Scott Yak
Hanna Mazzawi
Eugen Hotaj
Ghassen Jerfel
Vladimir Macko


AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention. Our framework is inspired by the AdaNet algorithm (Cortes et al., 2017) which learns the structure of a neural network as an ensemble of subnetworks. We designed it to: (1) integrate with the existing TensorFlow ecosystem, (2) offer sensible default search spaces to perform well on novel datasets, (3) present a flexible API to utilize expert information when available, and (4) efficiently accelerate training with distributed CPU, GPU, and TPU hardware. The code is open-source and available at https://github.com/tensorflow/adanet.

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