Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning is especially compelling due to the strong expressive power induced by the network. However, inference in GPs, whether with or without deep kernel learning, can be computationally challenging on large datasets. Here, we propose GP-Tree, a novel method for multi-class classification with Gaussian processes and deep kernel learning. We develop a tree-based hierarchical model in which each internal node of the tree fits a GP to the data using the Polya-Gamma augmentation scheme. As a result, our method scales well with both the number of classes and data size. We demonstrate our method effectiveness against other Gaussian process training baselines, and we show how our general GP approach is easily applied to incremental few-shot learning and reaches state-of-the-art performance.
If you use the contents of this project, please cite our paper. @article{achituve2021gptree, title={GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning}, author={Achituve, Idan and Navon, Aviv and Yemini, Yochai andChechik, Gal and Fetaya, Ethan}, journal={ICML 2021}, year={2021} }