{"type":"ScholarlyArticle","title":"Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation","authors":[{"name":"Shufeng Kong","slug":"shufeng-kong","given":"Shufeng","family":"Kong","affiliation":"Department of Computer Science , Cornell University , USA"},{"name":"Junwen Bai","slug":"junwen-bai","given":"Junwen","family":"Bai","affiliation":"Department of Computer Science , Cornell University , USA"},{"name":"Jae Hee Lee","slug":"jae-hee-lee","given":"Jae Hee","family":"Lee","affiliation":"School of Computer Science and Informatics , Cardiff University , UK"},{"name":"Di Chen","slug":"di-chen","given":"Di","family":"Chen","affiliation":"Department of Computer Science , Cornell University , USA"},{"name":"Andrew Allyn","slug":"andrew-allyn","given":"Andrew","family":"Allyn","affiliation":"Gulf of Maine Research Institute , USA"},{"name":"Michelle Stuart","slug":"michelle-stuart","given":"Michelle","family":"Stuart","affiliation":"Department of Ecology, Evolution, and Natural Resources , Rutgers University , USA"},{"name":"Malin Pinsky","slug":"malin-pinsky","given":"Malin","family":"Pinsky","affiliation":"Department of Ecology, Evolution, and Natural Resources , Rutgers University , USA"},{"name":"Katherine Mills","slug":"katherine-mills","given":"Katherine","family":"Mills","affiliation":"Gulf of Maine Research Institute , USA"},{"name":"Carla Gomes","slug":"carla-gomes","given":"Carla","family":"Gomes","affiliation":"Department of Computer Science , Cornell University , USA"}],"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.","doi":"10.24963/ijcai.2020/603","url":"https://ijcai.org/proceedings/2020/603","pdf":"https://ijcai.org/proceedings/2020/0603.pdf","markdown":"https://ijcai.org/proceedings/2020/603.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/603","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":603,"pages":{"first":4375,"last":4381},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Deep Learning","Machine Learning: Big data; Scalability","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25465/file_25465.m3u8","length_sec":304,"provider":"hls"}],"references":[{"position":1,"raw":"[Almasi et al., 2016] Afshin Almasi, Mohammad Reza Eshraghian, Abbas Moghimbeigi, Abbas Rahimi, Kazem Mohammad, and Sadegh Fallahigilan. 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