# AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

arXiv: https://arxiv.org/abs/1905.00080 · HTML: https://mlsanity.com/paper/1905.00080/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Online Ensemble Learning for Sector Rotation: A Gradient-Free Framework](https://mlsanity.com/paper/2304.09947/) (2023)
- [Improving the performance of bagging ensembles for data streams through mini-batching](https://mlsanity.com/paper/2112.09834/) (2021)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)

## Abstract (arXiv)

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.