{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:23:05Z","timestamp":1774545785869,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/603","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"4375-4381","source":"Crossref","is-referenced-by-count":15,"title":["Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation"],"prefix":"10.24963","author":[{"given":"Shufeng","family":"Kong","sequence":"first","affiliation":[{"name":"Cornell University"}]},{"given":"Junwen","family":"Bai","sequence":"additional","affiliation":[{"name":"Cornell University"}]},{"given":"Jae Hee","family":"Lee","sequence":"additional","affiliation":[{"name":"Cardiff University"}]},{"given":"Di","family":"Chen","sequence":"additional","affiliation":[{"name":"Cornell University"}]},{"given":"Andrew","family":"Allyn","sequence":"additional","affiliation":[{"name":"Gulf of Maine Research Institute"}]},{"given":"Michelle","family":"Stuart","sequence":"additional","affiliation":[{"name":"Rutgers University"}]},{"given":"Malin","family":"Pinsky","sequence":"additional","affiliation":[{"name":"Rutgers University"}]},{"given":"Katherine","family":"Mills","sequence":"additional","affiliation":[{"name":"Gulf of Maine Research Institute"}]},{"given":"Carla","family":"Gomes","sequence":"additional","affiliation":[{"name":"Cornell University"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:16:14Z","timestamp":1594260974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/603"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/603","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}