{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T16:49:06Z","timestamp":1762102146557,"version":"3.28.0"},"reference-count":32,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1109\/cdc.2017.8264638","type":"proceedings-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T20:30:57Z","timestamp":1516739457000},"page":"6499-6504","source":"Crossref","is-referenced-by-count":5,"title":["On the construction of probabilistic Newton-type algorithms"],"prefix":"10.1109","author":[{"given":"Adrian G.","family":"Wills","sequence":"first","affiliation":[]},{"given":"Thomas B.","family":"Schon","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref32","article-title":"Student-t processs as alternatives to Gaussian processes","author":"shah","year":"2014","journal-title":"Proc Int Conf Artificial Intelligence and Statistics (AISTATS)"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1137\/140954362"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1109\/CDC.2010.5717378"},{"key":"ref10","article-title":"Fast probabilistic optimization from noisy gradients","author":"hennig","year":"2013","journal-title":"International Conference on Machine Learning (ICML)"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1098\/rspa.2015.0142"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1137\/0601049"},{"key":"ref13","first-page":"133","article-title":"Introduction to Gaussian processes","author":"mackay","year":"1998","journal-title":"Neural Networks and Machine Learning"},{"year":"2006","author":"rasmussen","journal-title":"Gaussian Processes for Machine Learning","key":"ref14"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/CDC.2017.8264638"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1023\/A:1012771025575"},{"key":"ref17","first-page":"1","article-title":"Gaussian processes for global optimization","author":"osborne","year":"2009","journal-title":"Proc 3rd Int Conf Learning Intell Optimization"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/JPROC.2015.2494218"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.3384\/diss.diva-122396"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1198\/016214504000000151"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1093\/comjnl\/13.3.317"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1023\/A:1008935410038"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1093\/imamat\/6.1.76"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1090\/S0025-5718-1970-0274029-X"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1007\/0-387-28982-8","author":"capp\u00e9","year":"2005","journal-title":"Inference in Hidden Markov Models"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1090\/S0025-5718-1970-0258249-6"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1093\/comjnl\/6.2.163"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1090\/S0025-5718-1965-0198670-6"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1137\/140955501"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.2307\/2003239"},{"key":"ref1","first-page":"843","article-title":"Quasi-Newton methods: A new direction","volume":"14","author":"hennig","year":"2013","journal-title":"Journal of Machine Learning Research"},{"key":"ref20","article-title":"Probabilistic line searches for stochastic optimization","author":"mahsereci","year":"2015","journal-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS)"},{"key":"ref22","article-title":"A tutorial on particle filtering and smoothing: Fifteen years later","author":"doucet","year":"2011","journal-title":"Handbook of Nonlinear Filtering"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1049\/ip-f-2.1993.0015","article-title":"Novel approach to nonlinear\/non-Gaussian Bayesian state estimation","volume":"140","author":"gordon","year":"1993","journal-title":"IEE Proceedings on Radar and Signal Processing"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1214\/14-STS511"},{"key":"ref23","article-title":"Sequential Monte Carlo methods for system identification","author":"sch\u00f6n","year":"2015","journal-title":"Proc IFAC Symp System Identification (SYSID)"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1016\/j.automatica.2010.10.013"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.3182\/20140824-6-ZA-1003.00278"}],"event":{"name":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","start":{"date-parts":[[2017,12,12]]},"location":"Melbourne, Australia","end":{"date-parts":[[2017,12,15]]}},"container-title":["2017 IEEE 56th Annual Conference on Decision and Control (CDC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8253407\/8263624\/08264638.pdf?arnumber=8264638","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T18:30:46Z","timestamp":1570645846000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8264638\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/cdc.2017.8264638","relation":{},"subject":[],"published":{"date-parts":[[2017,12]]}}}