{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:52:22Z","timestamp":1784821942057,"version":"3.55.0"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","doi-asserted-by":"publisher","award":["200021 175627"],"award-info":[{"award-number":["200021 175627"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NCCR Automation Project","award":["51NF40_180545"],"award-info":[{"award-number":["51NF40_180545"]}]},{"name":"CSEM's Data Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Contr."],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1109\/tac.2022.3227907","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T13:37:02Z","timestamp":1670593022000},"page":"2848-2861","source":"Crossref","is-referenced-by-count":20,"title":["Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach"],"prefix":"10.1109","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0942-5032","authenticated-orcid":false,"given":"Paul","family":"Scharnhorst","sequence":"first","affiliation":[{"name":"CSEM, Neuchâtel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5122-1183","authenticated-orcid":false,"given":"Emilio T.","family":"Maddalena","sequence":"additional","affiliation":[{"name":"Automatic Control Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7145-0995","authenticated-orcid":false,"given":"Yuning","family":"Jiang","sequence":"additional","affiliation":[{"name":"Automatic Control Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7239-4799","authenticated-orcid":false,"given":"Colin N.","family":"Jones","sequence":"additional","affiliation":[{"name":"Automatic Control Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2021.109896"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492906270016"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2016.2539222"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1111\/j.1740-9713.2004.00050.x"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2182033"},{"key":"ref11","first-page":"2652","article-title":"Universal kernels","volume":"7","author":"micchelli","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref10","author":"sch\u00f6lkopf","year":"2002","journal-title":"Learning With Kernels Support Vector Machines Regularization Optimization and Beyond"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2019.2958840"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2753460"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2014.01.001"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2351851"},{"key":"ref51","first-page":"844","article-title":"On kernelized multi-armed bandits","author":"chowdhury","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8619572"},{"key":"ref46","first-page":"1","article-title":"Uniform error bounds for Gaussian process regression with application to safe control","author":"lederer","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref45","article-title":"Gaussian processes and kernel methods: A review on connections and equivalences","author":"kanagawa","year":"2018"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16912"},{"key":"ref47","first-page":"1","article-title":"Safe model-based reinforcement learning with stability guarantees","volume":"30","author":"berkenkamp","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1137\/13094829X"},{"key":"ref41","author":"boyd","year":"2011","journal-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177704840"},{"key":"ref43","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2019.2949757"},{"key":"ref8","article-title":"Every model learned by gradient descent is approximately a kernel machine","author":"domingos","year":"2020"},{"key":"ref7","first-page":"2388","article-title":"Mean-field theory of two-layers neural networks: Dimension-free bounds and kernel limit","author":"mei","year":"0","journal-title":"Proc Conf Learn Theory"},{"key":"ref9","first-page":"1","article-title":"Gaussian process behaviour in wide deep neural networks","author":"matthews","year":"0","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511617539"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/BF02432002"},{"key":"ref6","first-page":"1348","article-title":"Approximation beats concentration? An approximation view on inference with smooth radial kernels","author":"belkin","year":"0","journal-title":"Proc Conf Learn Theory"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05228-7"},{"key":"ref40","first-page":"1","article-title":"Convergence of sparse variational inference in Gaussian processes regression","volume":"21","author":"burt","year":"2020","journal-title":"J Mach Learn Res"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2020.109036"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2004.05.006"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2004.02.002"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1561\/2000000050"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3068889"},{"key":"ref30","first-page":"1015","article-title":"KPC: Learning-based model predictive control with deterministic guarantees","author":"maddalena","year":"0","journal-title":"Proc Learn Dyn Control"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2009.04.014"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2021.3049335"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511618796"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3501714.3501755"},{"key":"ref39","first-page":"592","article-title":"Divide and conquer kernel ridge regression","author":"zhang","year":"0","journal-title":"Proc Conf Learn Theory"},{"key":"ref38","first-page":"21","article-title":"Positive definite kernels: Past, present and future","volume":"4","author":"fasshauer","year":"2011","journal-title":"Dolomites Research Notes on Approximation"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2017.07.055"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2020.108914"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.2970146"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2019.06.032"},{"key":"ref20","first-page":"360","article-title":"Non-asymptotic kernel-based parametric estimation of continuous-time linear systems","volume":"61","author":"pin","year":"2016","journal-title":"IEEE Trans Autom Control"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2017.11.004"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2018.2791464"},{"key":"ref28","first-page":"465","article-title":"Parameter optimization for learning-based control of control-affine systems","author":"lederer","year":"0","journal-title":"Proc Learn Dyn Control"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2016.7487170"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2020.108948"}],"container-title":["IEEE Transactions on Automatic Control"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9\/10109211\/09978714.pdf?arnumber=9978714","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T15:07:00Z","timestamp":1684163220000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9978714\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":52,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tac.2022.3227907","relation":{},"ISSN":["0018-9286","1558-2523","2334-3303"],"issn-type":[{"value":"0018-9286","type":"print"},{"value":"1558-2523","type":"electronic"},{"value":"2334-3303","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]}}}