{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T16:51:31Z","timestamp":1777395091405,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["IIS-2006633"],"award-info":[{"award-number":["IIS-2006633"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539464","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"2020-2028","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models"],"prefix":"10.1145","author":[{"given":"Tyler","family":"Wilson","sequence":"first","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"McDonald","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asadullah Hill","family":"Galib","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pang-Ning","family":"Tan","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifeng","family":"Luo","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2009.80"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972801.57"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020910605990"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16834"},{"key":"e_1_3_2_2_5_1","volume-title":"Mixture density networks","author":"Bishop Christopher M.","year":"1994","unstructured":"Christopher M. Bishop. 1994. Mixture density networks. Neural Computing Research Group, Aston University (1994)."},{"key":"e_1_3_2_2_6_1","volume-title":"Xiao Xiang Zhu, and Markus Reichstein","author":"Camps-Valls Gustau","year":"2021","unstructured":"Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, and Markus Reichstein. 2021. Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. John Wiley & Sons."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.09.060"},{"key":"e_1_3_2_2_8_1","volume-title":"An Introduction to Statistical Modeling of Extreme Values","author":"Coles Stuart","unstructured":"Stuart Coles. 2001. An Introduction to Statistical Modeling of Extreme Values. Springer."},{"key":"e_1_3_2_2_9_1","volume-title":"Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica: Journal of the Econometric Society","author":"Cragg John G","year":"1971","unstructured":"John G Cragg. 1971. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica: Journal of the Econometric Society (1971), 829--844."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330896"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/413"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103531"},{"key":"e_1_3_2_2_14_1","volume-title":"Proceedings of the Third International Conference on Learning Representations (ICLR '15)","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In Proceedings of the Third International Conference on Learning Representations (ICLR '15)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/603"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.2307\/1269547"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/462"},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of the Thirty-First Conference on Neural Information Processing Systems (NeurIPS '17)","author":"Racah Evan","year":"2017","unstructured":"Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, and Christopher Pal. 2017. ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. In Proceedings of the Thirty-First Conference on Neural Information Processing Systems (NeurIPS '17)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1029\/2020MS002203"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Suman Ravuri Karel Lenc Matthew Willson Dmitry Kangin Remi Lam Piotr Mirowski Megan Fitzsimons Maria Athanassiadou Sheleem Kashem Sam Madge et al. 2021. Skilful precipitation nowcasting using deep generative models of radar. Nature 597 7878 (2021) 672--677.","DOI":"10.1038\/s41586-021-03854-z"},{"key":"e_1_3_2_2_21_1","volume-title":"A spatio-temporal Poisson hurdle point process to model wildfires. Stochastic environmental research and risk assessment 28, 7","author":"Serra Laura","year":"2014","unstructured":"Laura Serra, Marc Saez, Pablo Juan, Diego Varga, and Jorge Mateu. 2014. A spatio-temporal Poisson hurdle point process to model wildfires. Stochastic environmental research and risk assessment 28, 7 (2014), 1671--1684."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_2_2_23_1","volume-title":"Convnet architecture search for spatiotemporal feature learning. arXiv:1708.05038","author":"Tran Du","year":"2017","unstructured":"Du Tran, Jamie Ray, Zheng Shou, Shih-Fu Chang, and Manohar Paluri. 2017. Convnet architecture search for spatiotemporal feature learning. arXiv:1708.05038 (2017)."},{"key":"e_1_3_2_2_24_1","volume-title":"Proceedings of the Twenty-Fourth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18)","author":"Vandal Thomas","unstructured":"Thomas Vandal, Evan Kodra, Jennifer Dy, Sangram Ganguly, Ramakrishna Nemani, and Auroop R. Ganguly. 2018. Quantifying uncertainty in discretecontinuous and skewed data with Bayesian deep learning. In Proceedings of the Twenty-Fourth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). 2377--2386."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-017-9793-z"},{"key":"e_1_3_2_2_26_1","volume-title":"Proceedings of the Sixth International Conference on Learning Representations (ICLR '18)","author":"Zhang Huan","year":"2018","unstructured":"Tsui-WeiWeng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel. 2018. Evaluating the robustness of neural networks: An extreme value theory approach. In Proceedings of the Sixth International Conference on Learning Representations (ICLR '18)."},{"key":"e_1_3_2_2_27_1","volume-title":"Deep Learning-Based Open Set Fault Diagnosis by Extreme Value Theory","author":"Yu Xiaolei","year":"2021","unstructured":"Xiaolei Yu, Zhibin Zhao, Xingwu Zhang, Qiyang Zhang, Yilong Liu, Chuang Sun, and Xuefeng Chen. 2021. Deep Learning-Based Open Set Fault Diagnosis by Extreme Value Theory. IEEE Transactions on Industrial Informatics (2021)."},{"key":"e_1_3_2_2_28_1","volume-title":"Proceedings of the Sixth International Conference on Learning Representations (ICLR '18)","author":"Zong Bo","year":"2018","unstructured":"Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In Proceedings of the Sixth International Conference on Learning Representations (ICLR '18)."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539464","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539464","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539464","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:03Z","timestamp":1750186983000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539464"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":28,"alternative-id":["10.1145\/3534678.3539464","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539464","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}