{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:15:42Z","timestamp":1759385742115,"version":"3.28.0"},"reference-count":21,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.23919\/icif.2018.8455284","type":"proceedings-article","created":{"date-parts":[[2018,9,6]],"date-time":"2018-09-06T22:47:48Z","timestamp":1536274068000},"page":"47-54","source":"Crossref","is-referenced-by-count":6,"title":["Sparse Structure Enabled Grid Spectral Mixture Kernel for Temporal Gaussian Process Regression"],"prefix":"10.23919","author":[{"given":"Feng","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwei","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lishuo","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianshi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi-Quan Tom","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergios","family":"Theodoridis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300014908"},{"journal-title":"Distributed Gaussian Processes","year":"2015","author":"deisenroth","key":"ref11"},{"key":"ref12","first-page":"1939","article-title":"A unifying view of sparse approximate Gaussian process regression","volume":"6","author":"candela","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref13","first-page":"567","article-title":"Variational learning of inducing variables in sparse Gaussian processes","author":"titsias","year":"2009","journal-title":"Proc Artificial Intelligence and Statistics"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1137\/090771806"},{"key":"ref15","article-title":"Continuous-time DC kernel: a stable generalized first-order spline kernel","author":"chen","year":"2017","journal-title":"IEEE Transactions on Automatic Control"},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2018.04.035","article-title":"On asymptotic properties of hyperparameter estimators for kernel-based regularization methods","author":"mu","year":"2018","journal-title":"Automatica"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2017.12.039","article-title":"On kernel design for regularized LTI system identification","author":"chen","year":"2018","journal-title":"Automatica"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2351851"},{"key":"ref19","first-page":"367","article-title":"On kernel-target alignment","volume":"14","author":"cristianini","year":"2002","journal-title":"Advances in neural information processing systems"},{"key":"ref4","first-page":"1166","article-title":"Structure discovery in nonparametric regression through compositional kernel search","author":"duvenaud","year":"2013","journal-title":"Proceedings of the 30th International Conference on Machine Learning"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2017.8254808"},{"journal-title":"Covariance kernels for fast automatic pattern discovery and extrapolation with gaussian processes","year":"2014","author":"wilson","key":"ref6"},{"key":"ref5","first-page":"1067","article-title":"Gaussian process kernels for pattern discovery and extrapolation","author":"wilson","year":"2013","journal-title":"Proceedings of the 30th International Conference on Machine Learning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2448083"},{"key":"ref7","first-page":"682","article-title":"Using the Nystr&#x00F6;m method to speed up kernel machines","author":"williams","year":"2001","journal-title":"Advances in neural information processing systems"},{"key":"ref2","first-page":"3901","article-title":"Predicting spatio-temporal propagation of seasonal influenza using variational Gaussian process regression","author":"senanayake","year":"2016","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"journal-title":"Gaussian Processes for Machine Learning","year":"2006","author":"rasmussen","key":"ref1"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2246292"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2009.09.020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2004.831016"}],"event":{"name":"2018 21st International Conference on Information Fusion (FUSION 2018)","start":{"date-parts":[[2018,7,10]]},"location":"Cambridge","end":{"date-parts":[[2018,7,13]]}},"container-title":["2018 21st International Conference on Information Fusion (FUSION)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8442112\/8454975\/08455284.pdf?arnumber=8455284","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T11:59:11Z","timestamp":1643198351000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8455284\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":21,"URL":"https:\/\/doi.org\/10.23919\/icif.2018.8455284","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}