Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Zhenwen Dai, Mauricio Álvarez, Neil Lawrence
Abstract
Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a model that captures the latent information related to these conditions and generalize to a new one with few data? We present a new model called Latent Variable Multiple Output Gaussian Processes (LVMOGP) that allows to jointly model multiple conditions for regression and generalize to a new condition with a few data points at test time. LVMOGP infers the posteriors of Gaussian processes together with a latent space representing the information about different conditions. We derive an efficient variational inference method for LVMOGP for which the computational complexity is as low as sparse Gaussian processes. We show that LVMOGP significantly outperforms related Gaussian process methods on various tasks with both synthetic and real data.
BibTeX
@inproceedings{NIPS2017_1680e9fa,
author = {Dai, Zhenwen and \'{A}lvarez, Mauricio and Lawrence, Neil},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/1680e9fa7b4dd5d62ece800239bb53bd-Paper.pdf},
volume = {30},
year = {2017}
}