{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:22:07Z","timestamp":1776885727094,"version":"3.51.2"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,14]]},"DOI":"10.1109\/icassp48485.2024.10447126","type":"proceedings-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T18:56:31Z","timestamp":1710788191000},"page":"12881-12885","source":"Crossref","is-referenced-by-count":4,"title":["Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models"],"prefix":"10.1109","author":[{"given":"Zhidi","family":"Lin","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong,Shenzhen,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Maro\u00f1as","sequence":"additional","affiliation":[{"name":"The University of Hong Kong,HKSAR"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Autonomous University of Madrid, and Cognizant,Machine Learning Group,Madrid,Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yin","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong,Shenzhen,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergios","family":"Theodoridis","sequence":"additional","affiliation":[{"name":"National and Kapodistrian University of Athens,Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"3156","article-title":"Bayesian inference and learning in Gaussian process statespace models with particle MCMC","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Frigola"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10779"},{"key":"ref3","first-page":"11338","article-title":"Attentive state-space modeling of disease progression","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Alaa"},{"key":"ref4","first-page":"3680","article-title":"Variational Gaussian process state-space models","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Frigola"},{"key":"ref5","article-title":"Bayesian time series learning with Gaussian processes","volume-title":"Ph.D. dissertation","author":"Frigola","year":"2015"},{"key":"ref6","first-page":"1280","article-title":"Probabilistic recurrent state-space models","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Doerr"},{"key":"ref7","first-page":"2931","article-title":"Overcoming mean-field approximations in recurrent Gaussian process models","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Ialongo"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.23919\/FUSION49751.2022.9841347"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3023008"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139344203"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2022.3198201"},{"key":"ref13","volume-title":"Machine Learning: A Bayesian and Optimization Perspective","author":"Theodoridis","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1167"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.23919\/FUSION45008.2020.9190598"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/OJSP.2020.3036276"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2949508"},{"key":"ref18","first-page":"5309","article-title":"Identification of Gaussian process state space models","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Eleftheriadis"},{"key":"ref19","article-title":"Laplace approximated Gaussian process state-space models","volume-title":"Proc. Conf. Uncertain. Artif. Intell. (UAI)","author":"Lindinger"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095784"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/icassp49357.2023.10095784"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.23919\/Eusipco47968.2020.9287481"},{"key":"ref23","first-page":"9603","article-title":"Free-form variational inference for Gaussian process state-space models","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Fan"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746570"},{"key":"ref25","first-page":"1081","article-title":"Transforming Gaussian processes with normalizing flows","volume-title":"Proc. Int. Conf. Artif. Intell. Stat. (AISTATS)","author":"Maro\u00f1as"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"ref27","first-page":"24045","article-title":"Efficient transformed Gaussian processes for non-stationary dependent multi-class classification","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Maro\u00f1as"},{"key":"ref28","first-page":"282","article-title":"Gaussian processes for big data","volume-title":"Proc. Conf. Uncertain. Artif. Intell. (UAI)","author":"Hensman"},{"key":"ref29","first-page":"2460","article-title":"Copula processes","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Wilson"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.06.012"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1561\/2200000056"}],"event":{"name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Seoul, Korea, Republic of","start":{"date-parts":[[2024,4,14]]},"end":{"date-parts":[[2024,4,19]]}},"container-title":["ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10445798\/10445803\/10447126.pdf?arnumber=10447126","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T05:30:15Z","timestamp":1722576615000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10447126\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/icassp48485.2024.10447126","relation":{},"subject":[],"published":{"date-parts":[[2024,4,14]]}}}