{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:21:43Z","timestamp":1776882103850,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS-1539608"],"award-info":[{"award-number":["IIS-1539608"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS-1254206"],"award-info":[{"award-number":["IIS-1254206"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330881","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"324-334","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":63,"title":["CoSTCo"],"prefix":"10.1145","author":[{"given":"Hanpeng","family":"Liu","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"given":"Yaguang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"given":"Michael","family":"Tsang","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2010.08.004"},{"key":"e_1_3_2_1_2_1","volume-title":"JMLR","volume":"12","author":"\u00c1lvarez Mauricio A","year":"2011"},{"key":"e_1_3_2_1_3_1","volume-title":"Qi Rose Yu, and Yan Liu","author":"Bahadori Mohammad Taha","year":"2014"},{"key":"e_1_3_2_1_4_1","volume-title":"SPALS: Fast alternating least squares via implicit leverage scores sampling. In NIPS. 721--729.","author":"Cheng Dehua","year":"2016"},{"key":"e_1_3_2_1_5_1","unstructured":"Wei Chu and Zoubin Ghahramani. 2009. Probabilistic models for incomplete multi-dimensional arrays. In AISTATS. 89--96.  Wei Chu and Zoubin Ghahramani. 2009. Probabilistic models for incomplete multi-dimensional arrays. In AISTATS. 89--96."},{"key":"e_1_3_2_1_6_1","first-page":"137","article-title":"Nonnegative matrix factorization and probabilistic latent semantic indexing: Equivalence chi-square statistic, and a hybrid method","volume":"42","author":"Ding Chris","year":"2006","journal-title":"AAAI"},{"key":"e_1_3_2_1_7_1","volume-title":"On the Power of Over-parametrization in Neural Networks with Quadratic Activation. In International Conference on Machine Learning. 1328--1337","author":"Du Simon S","year":"2018"},{"key":"e_1_3_2_1_8_1","volume-title":"Neural network matrix factorization. arXiv preprint arXiv:1511.06443","author":"Dziugaite Gintare Karolina","year":"2015"},{"key":"e_1_3_2_1_9_1","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"Martin Ester","year":"1996","journal-title":"KDD"},{"key":"e_1_3_2_1_10_1","unstructured":"Mart'in Abadi et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. In OSDI. Software available from tensorflow.org.   Mart'in Abadi et al. 2016. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. In OSDI. Software available from tensorflow.org."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Xiaomin Fang Rong Pan Guoxiang Cao Xiuqiang He and Wenyuan Dai. 2015. Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel.. In AAAI. 439--445.   Xiaomin Fang Rong Pan Guoxiang Cao Xiuqiang He and Wenyuan Dai. 2015. Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel.. In AAAI. 439--445.","DOI":"10.1609\/aaai.v29i1.9214"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/0266-5611\/27\/2\/025010"},{"key":"e_1_3_2_1_13_1","unstructured":"Benjamin Haeffele Eric Young and Rene Vidal. 2014. Structured Low-Rank Matrix Factorization: Optimality Algorithm and Applications to Image Processing. In ICML. 2007--2015.   Benjamin Haeffele Eric Young and Rene Vidal. 2014. Structured Low-Rank Matrix Factorization: Optimality Algorithm and Applications to Image Processing. In ICML. 2007--2015."},{"key":"e_1_3_2_1_14_1","unstructured":"Richard A Harshman. 1970. Foundations of the PARAFAC procedure: Models and conditions for an\" explanatory\" multimodal factor analysis. (1970).  Richard A Harshman. 1970. Foundations of the PARAFAC procedure: Models and conditions for an\" explanatory\" multimodal factor analysis. (1970)."},{"key":"e_1_3_2_1_15_1","volume-title":"Dusk: A dual structure-preserving kernel for supervised tensor learning with applications to neuroimages","author":"He Lifang","year":"2014"},{"key":"e_1_3_2_1_16_1","unstructured":"Lifang He Chun-Ta Lu Guixiang Ma Shen Wang Linlin Shen S Yu Philip and Ann B Ragin. 2017. Kernelized support tensor machines. In ICML. 1442--1451.   Lifang He Chun-Ta Lu Guixiang Ma Shen Wang Linlin Shen S Yu Philip and Ann B Ragin. 2017. Kernelized support tensor machines. In ICML. 1442--1451."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623658"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339583"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959165"},{"key":"e_1_3_2_1_20_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1137\/07070111X"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553452"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767722"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298558"},{"key":"e_1_3_2_1_25_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In ICLR .  Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In ICLR ."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623638"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-018-9587-4"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.39"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.14778\/3115404.3115407"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-012-0280-z"},{"key":"e_1_3_2_1_31_1","volume-title":"Scalable tucker factorization for sparse tensors-algorithms and discoveries","author":"Oh Sejoon"},{"key":"e_1_3_2_1_32_1","volume-title":"Mr Prabhat, and Chris Pal.","author":"Racah Evan","year":"2017"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718498"},{"key":"e_1_3_2_1_34_1","unstructured":"Bernardino Romera-Paredes Hane Aung Nadia Bianchi-Berthouze and Massimiliano Pontil. 2013. Multilinear multitask learning. In ICML. 1444--1452.   Bernardino Romera-Paredes Hane Aung Nadia Bianchi-Berthouze and Massimiliano Pontil. 2013. Multilinear multitask learning. In ICML. 1444--1452."},{"key":"e_1_3_2_1_35_1","unstructured":"Bernardino Romera-Paredes and Massimiliano Pontil. 2013. A new convex relaxation for tensor completion. In NIPS. 2967--2975.   Bernardino Romera-Paredes and Massimiliano Pontil. 2013. A new convex relaxation for tensor completion. In NIPS. 2967--2975."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102451"},{"key":"e_1_3_2_1_37_1","unstructured":"Richard Socher Danqi Chen Christopher D Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In NIPS. 926--934.   Richard Socher Danqi Chen Christopher D Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In NIPS. 926--934."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098007"},{"key":"e_1_3_2_1_39_1","unstructured":"Nitish Srivastava Elman Mansimov and Ruslan Salakhudinov. 2015. Unsupervised learning of video representations using lstms. In ICML. 843--852.   Nitish Srivastava Elman Mansimov and Ruslan Salakhudinov. 2015. Unsupervised learning of video representations using lstms. In ICML. 843--852."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289464"},{"key":"e_1_3_2_1_41_1","volume-title":"Neural Tensor Factorization. arXiv preprint arXiv:1802.04416","author":"Wu Xian","year":"2018"},{"key":"e_1_3_2_1_42_1","unstructured":"Zenglin Xu Feng Yan etal 2011. Infinite Tucker decomposition: Nonparametric Bayesian models for multiway data analysis. arXiv:1108.6296 (2011).  Zenglin Xu Feng Yan et al. 2011. Infinite Tucker decomposition: Nonparametric Bayesian models for multiway data analysis. arXiv:1108.6296 (2011)."},{"key":"e_1_3_2_1_43_1","unstructured":"Rose Yu Dehua Cheng and Yan Liu. 2015. Accelerated online low rank tensor learning for multivariate spatiotemporal streams. In ICML. 238--247.   Rose Yu Dehua Cheng and Yan Liu. 2015. Accelerated online low rank tensor learning for multivariate spatiotemporal streams. In ICML. 238--247."},{"key":"e_1_3_2_1_44_1","unstructured":"Rose Yu Guangyu Li and Yan Liu. 2018. Tensor Regression Meets Gaussian Processes. In AISTATS. 482--490.  Rose Yu Guangyu Li and Yan Liu. 2018. Tensor Regression Meets Gaussian Processes. In AISTATS. 482--490."},{"key":"e_1_3_2_1_45_1","unstructured":"Rose Yu and Yan Liu. 2016. Learning from multiway data: Simple and efficient tensor regression. In ICML. 373--381.   Rose Yu and Yan Liu. 2016. Learning from multiway data: Simple and efficient tensor regression. In ICML. 373--381."},{"key":"e_1_3_2_1_46_1","unstructured":"Chiyuan Zhang et al. 2016. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016).  Chiyuan Zhang et al. 2016. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)."},{"key":"e_1_3_2_1_47_1","unstructured":"Shandian Zhe Zenglin Xu Xinqi Chu Yuan Qi and Youngja Park. 2015. Scalable nonparametric multiway data analysis. In AISTATS. 1125--1134.  Shandian Zhe Zenglin Xu Xinqi Chu Yuan Qi and Youngja Park. 2015. Scalable nonparametric multiway data analysis. In AISTATS. 1125--1134."},{"key":"e_1_3_2_1_48_1","unstructured":"Shandian Zhe Kai Zhang Pengyuan Wang Kuang-chih Lee Zenglin Xu Yuan Qi and Zoubin Ghahramani. 2016. Distributed flexible nonlinear tensor factorization. In NIPS. 928--936.   Shandian Zhe Kai Zhang Pengyuan Wang Kuang-chih Lee Zenglin Xu Yuan Qi and Zoubin Ghahramani. 2016. Distributed flexible nonlinear tensor factorization. In NIPS. 928--936."}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","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 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330881","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330881","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330881","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:03Z","timestamp":1750206363000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330881"}},"subtitle":["A Neural Tensor Completion Model for Sparse Tensors"],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":48,"alternative-id":["10.1145\/3292500.3330881","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330881","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}