@inproceedings{d6c7dfad079f4f8a8df4675fd6a6b426,
title = "Deep hurdle networks for zero-inflated multi-target regression: Application to multiple species abundance estimation",
abstract = "A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to the absence of species for a large number of sites. The problem of tackling both issues simultaneously, which we refer to as the zero-inflated multi-target regression problem, has not been addressed by previous methods in statistics and machine learning. In this paper, we propose a novel deep model for the zero-inflated multi-target regression problem. To this end, we first model the joint distribution of multiple response variables as a multivariate probit model and then couple the positive outcomes with a multivariate log-normal distribution. By penalizing the difference between the two distributions' covariance matrices, a link between both distributions is established. The whole model is cast as an end-to-end learning framework and we provide an efficient learning algorithm for our model that can be fully implemented on GPUs. We show that our model outperforms the existing state-of-the-art baselines on two challenging real-world species distribution datasets concerning bird and fish populations.",
author = "Shufeng Kong and Junwen Bai and Lee, {Jae Hee} and Di Chen and Andrew Allyn and Michelle Stuart and Malin Pinsky and Katherine Mills and Gomes, {Carla P.}",
note = "Funding Information: This work was partially supported by National Science Foundation OIA-1936950 and CCF-1522054, and the work of Jae Hee Lee was supported by European Research Council Starting Grant 637277. We thank the Cornell Lab of Ornithology and Gulf of Maine Research Institute for providing data, resources and advice. Lastly, we thank Richard Bernstein for proofreading the paper. Publisher Copyright: {\textcopyright} 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.; 29th International Joint Conference on Artificial Intelligence, IJCAI 2020 ; Conference date: 01-01-2021",
year = "2020",
language = "English (US)",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "4375--4381",
editor = "Christian Bessiere",
booktitle = "Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020",
}