{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T01:37:36Z","timestamp":1745631456877,"version":"3.37.3"},"reference-count":74,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi Provincial Fund","award":["2020JM-185"]},{"name":"National Natural Science Foundation of China","award":["62171338"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"This paper considers trajectory a modeling problem for a multi-agent system by using the Gaussian processes. The Gaussian process, as the typical data-driven method, is well suited to characterize the model uncertainties and perturbations in a complex environment. To address model uncertainties and noises disturbances, a distributed Gaussian process is proposed to characterize the system model by using local information exchange among neighboring agents, in which a number of agents cooperate without central coordination to estimate a common Gaussian process function based on local measurements and datum received from neighbors. In addition, both the continuous-time system model and the discrete-time system model are considered, in which we design a control Lyapunov function to learn the continuous-time model, and a distributed model predictive control-based approach is used to learn the discrete-time model. Furthermore, we apply a Kullback\u2013Leibler average consensus fusion algorithm to fuse the local prediction results (mean and variance) of the desired Gaussian process. The performance of the proposed distributed Gaussian process is analyzed and is verified by two trajectory tracking examples.<\/jats:p>","DOI":"10.3390\/s22207887","type":"journal-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T04:31:01Z","timestamp":1666067461000},"page":"7887","source":"Crossref","is-referenced-by-count":2,"title":["Trajectory Modeling by Distributed Gaussian Processes in Multiagent Systems"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1948-7811","authenticated-orcid":false,"given":"Dongjin","family":"Xin","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2638-9643","authenticated-orcid":false,"given":"Lingfeng","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Beckers, T., Umlauft, J., Kulic, D., and Hirche, S. (2017, January 12\u201315). Stable Gaussian process based tracking control of Lagrangian systems. Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, Australia.","DOI":"10.1109\/CDC.2017.8264427"},{"key":"ref_2","unstructured":"Corke, P.I., and Khatib, O. (2011). Robotics, Vision and Control: Fundamental Algorithms in MATLAB, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1109\/TIE.2017.2722424","article-title":"Adaptive-critic-based robust trajectory tracking of uncertain dynamics and its application to a spring-mass-damper system","volume":"65","author":"Wang","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1109\/TCSI.2016.2553318","article-title":"Area-efficient approach for generating quantized gaussian noise","volume":"63","author":"Choi","year":"2016","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1109\/TRO.2011.2159412","article-title":"Learning stable nonlinear dynamical systems with Gaussian mixture models","volume":"27","author":"Billard","year":"2011","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7717","DOI":"10.1109\/JIOT.2020.3040676","article-title":"Data-aided sensing for Gaussian process regression in iot systems","volume":"8","author":"Choi","year":"2021","journal-title":"IEEE Internet Things"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1109\/JIOT.2018.2840129","article-title":"Clustering of data streams with dynamic Gaussian Mixture Models: An IoT application in industrial processes","volume":"5","author":"Bielza","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TIE.2017.2714127","article-title":"Short-term solar power forecasting based on weighted Gaussian process regression","volume":"65","author":"Sheng","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8540","DOI":"10.1109\/TIE.2020.3018078","article-title":"Modeling and analysis of permanent magnet spherical motors by a multi-task Gaussian process method and finite element method for output torque","volume":"68","author":"Wen","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3180","DOI":"10.1109\/TCYB.2018.2842783","article-title":"Fault tolerant nonrepetitive trajectory tracking for mimo output constrained nonlinear systems using iterative learning control","volume":"49","author":"Jin","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3806","DOI":"10.1109\/TCYB.2018.2856269","article-title":"A kinematic model for swarm finite-time trajectory tracking","volume":"49","author":"Fedele","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_12","unstructured":"Wilson, A.G., Knowles, D.A., and Ghahramani, Z. (July, January 26). Gaussian process regression networks. Proceedings of the 29th International Conference on Machine Learning, Edinburgh, UK."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.1109\/TPAMI.2018.2836422","article-title":"Distributed multi-agent gaussian regression via finite-dimensional approximations","volume":"41","author":"Pillonetto","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2468","DOI":"10.1016\/j.automatica.2012.06.080","article-title":"Distributed parametric and nonparametric regression with on-line performance bounds computation","volume":"48","author":"Varagnolo","year":"2012","journal-title":"Automatica"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.isatra.2020.10.011","article-title":"Simulation of variational Gaussian process NARX models with GPGPU","volume":"109","author":"Krivec","year":"2021","journal-title":"ISA Trans."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.isatra.2007.04.001","article-title":"Application of Gaussian processes for black-box modelling of biosystems","volume":"46","author":"Aman","year":"2007","journal-title":"ISA Trans."},{"key":"ref_17","first-page":"5537","article-title":"Variational fourier features for Gaussian processes","volume":"18","author":"Hensman","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","first-page":"1425","article-title":"Variational inference for latent variables and uncertain inputs in Gaussian processes","volume":"17","author":"Damianou","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1016\/j.automatica.2013.04.040","article-title":"Robust cooperative tracking for multiple non-identical second-order nonlinear systems","volume":"49","author":"Meng","year":"2013","journal-title":"Automatica"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1002\/acs.3253","article-title":"Distributed Kalman filter for linear system with complex multichannel stochastic uncertain parameter and decoupled local filters","volume":"35","author":"Pu","year":"2021","journal-title":"Int. J. Adapt. Control. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1109\/TAES.2021.3117896","article-title":"Adaptive Kalman filtering for recursive both additive noise and multiplicative noise","volume":"58","author":"Yu","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/TCNS.2021.3061941","article-title":"Bearing-based distributed formation control of multiple vertical take-off and landing UAVs","volume":"8","author":"Huang","year":"2021","journal-title":"IEEE Trans. Control. Netw. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.arcontrol.2019.05.006","article-title":"A survey of distributed optimization","volume":"47","author":"Yang","year":"2019","journal-title":"Annu. Rev. Control."},{"key":"ref_24","first-page":"683249","article-title":"Distributed filter with consensus strategies for sensor networks","volume":"2013","author":"Li","year":"2013","journal-title":"J. Appl. Math."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1109\/TAC.2013.2266857","article-title":"Coordinated one-step optimal distributed state prediction for a networked dynamical system","volume":"58","author":"Zhou","year":"2013","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.automatica.2013.11.042","article-title":"Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability","volume":"50","author":"Battistelli","year":"2014","journal-title":"Automatica"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Umlauft, J., Lederer, A., and Hirche, S. (2017, January 24\u201326). Learning stable Gaussian process state space models. Proceedings of the 2017 American Control Conference (ACC), Seattle, DC, USA.","DOI":"10.23919\/ACC.2017.7963165"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jagtap, P., Pappas, G.J., and Zamani, M. (2020, January 14\u201318). Control barrier functions for unknown nonlinear systems using Gaussian processes. Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC), Jeju Island, Korea.","DOI":"10.1109\/CDC42340.2020.9303847"},{"key":"ref_29","first-page":"8","article-title":"Uncertainty-based Human Motion Tracking with Stable Gaussian Process State Space Models","volume":"51","author":"Umlauft","year":"2019","journal-title":"IFAC-Pap."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1109\/LCSYS.2018.2841961","article-title":"An uncertainty-based control Lyapunov approach for control-affine systems modeled by Gaussian process","volume":"2","author":"Umlauft","year":"2018","journal-title":"IEEE Control. Syst. Lett."},{"key":"ref_31","first-page":"659","article-title":"Uniform error bounds for Gaussian process regression with application to safe control","volume":"32","author":"Lederer","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","unstructured":"Deisenroth, M., and Ng, J.W. (2015, January 7\u20139). Distributed Gaussian processes. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/LSP.2019.2925532","article-title":"Distributed Gaussian processes hyperparameter optimization for big data using proximal ADMM","volume":"26","author":"Xie","year":"2019","journal-title":"IEEE Signal Processing Lett."},{"key":"ref_34","unstructured":"Bonilla, E.V., Chai, K.M., and Williams, C. (2008, January 3\u20135). Multi-task Gaussian process prediction. Proceedings of the Advances in Neural Information Processing Systems 20 (NIPS 2007), Vancouver, BC, Canada."},{"key":"ref_35","unstructured":"Alvarez, M., and Lawrence, N.D. (2009, January 8). Sparse convolved Gaussian processes for multi-output regression. Proceedings of the Advances in Neural Information Processing Systems 21 (NIPS 2008), Vancouver, BC, Canada."},{"key":"ref_36","unstructured":"Gal, Y., van der Wilk, M., and Rasmussen, C.E. (2014, January 8\u201313). Distributed variational inference in sparse Gaussian process regression and latent variable models. Proceedings of the Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nerurkar, E.D., Roumeliotis, S.I., and Martinelli, A. (2009, January 6). Distributed maximum a posteriori estimation for multi-robot cooperative localization. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152398"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Franceschelli, M., and Gasparri, A. (2010, January 15). On agreement problems with gossip algorithms in absence of common reference frames. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509788"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cunningham, A., Indelman, V., and Dellaert, F. (2013, January 6\u201310). DDF-SAM 2.0: Consistent distributed smoothing and mapping. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631323"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1137\/100792366","article-title":"Formal theory of noisy sensor network localization","volume":"24","author":"Anderson","year":"2010","journal-title":"SIAM J. Discret. Math."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/TCNS.2014.2353512","article-title":"An asynchronous consensus-based algorithm for estimation from noisy relative measurements","volume":"1","author":"Carron","year":"2014","journal-title":"IEEE Trans. Control. Netw. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Thunberg, J., Montijano, E., and Hu, X. (2011, January 12\u201315). Distributed attitude synchronization control. Proceedings of the 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, USA.","DOI":"10.1109\/CDC.2011.6161295"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.automatica.2012.09.014","article-title":"On frame and orientation localization for relative sensing networks","volume":"49","author":"Piovan","year":"2013","journal-title":"Automatica"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1137\/060673400","article-title":"Consensus optimization on manifolds","volume":"48","author":"Sarlette","year":"2009","journal-title":"SIAM J. Control. Optim."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Choudhary, S., Carlone, L., Nieto, C., Rogers, J., Christensen, H.I., and Dellaert, F. (2016, January 16\u201321). Distributed trajectory estimation with privacy and communication constraints: A two-stage distributed Gauss-seidel approach. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487736"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TPAMI.2013.218","article-title":"Gaussian processes for data-efficient learning in robotics and control","volume":"37","author":"Deisenroth","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1109\/LRA.2018.2794582","article-title":"An efficient algorithm for optimal trajectory generation for heterogeneous multi-agent systems in non-convex environments","volume":"3","author":"Robinson","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1109\/TPAMI.2007.1167","article-title":"Gaussian process dynamical models for human motion","volume":"30","author":"Wang","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/MCS.2014.2320397","article-title":"Distributed model predictive control: An overview and roadmap of future research opportunities","volume":"34","author":"Negenborn","year":"2014","journal-title":"IEEE Control. Syst. Mag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.jprocont.2010.11.004","article-title":"Cooperative distributed model predictive control for nonlinear systems","volume":"21","author":"Stewart","year":"2011","journal-title":"J. Process Control."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/j.automatica.2013.01.019","article-title":"Cooperative distributed MPC for tracking","volume":"49","author":"Ferramosca","year":"2013","journal-title":"Automatica"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.automatica.2016.02.009","article-title":"Distributed synthesis and stability of cooperative distributed model predictive control for linear systems","volume":"69","author":"Conte","year":"2016","journal-title":"Automatica"},{"key":"ref_53","first-page":"275","article-title":"A cooperative distributed MPC algorithm with event-based communication and parallel optimization","volume":"3","author":"Stursberg","year":"2015","journal-title":"IEEE Trans. Control. Netw. Syst."},{"key":"ref_54","first-page":"216","article-title":"Coordinated non-cooperative distributed model predictive control for decoupled systems using graphs","volume":"49","author":"Alrifaee","year":"2016","journal-title":"IFAC-Pap."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Alonso, C.A., and Matni, N. (2020, January 14\u201318). Distributed and localized closed loop model predictive control via system level synthesis. Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC), Jeju, Korea.","DOI":"10.1109\/CDC42340.2020.9303936"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Alonso, C.A., Matni, N., and Anderson, J. (2020, January 14\u201318). Explicit distributed and localized model predictive control via system level synthesis. Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC), Jeju, Korea.","DOI":"10.1109\/CDC42340.2020.9304349"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/LRA.2018.2890572","article-title":"Trajectory generation for multiagent point-to-point transitions via distributed model predictive control","volume":"4","author":"Luis","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3769","DOI":"10.1109\/LRA.2021.3061307","article-title":"Data-driven MPC for quadrotors","volume":"6","author":"Torrente","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"105210","DOI":"10.1016\/j.sysconle.2022.105210","article-title":"Robust adaptive trajectory tracking for wheeled mobile robots based on Gaussian process regression","volume":"163","author":"Liu","year":"2022","journal-title":"Syst. Control. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"18301","DOI":"10.1109\/TITS.2022.3154926","article-title":"Tracking Dependent Extended Targets Using Multi-Output Spatiotemporal Gaussian Processes","volume":"23","author":"Akbari","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hidalgo-Carri\u00f3, J., Hennes, D., Schwendner, J., and Kirchner, F. (June, January 29). Gaussian process estimation of odometry errors for localization and mapping. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989670"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Brossard, M., and Bonnabel, S. (2019, January 20\u201324). Learning wheel odometry and IMU errors for localization. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794237"},{"key":"ref_63","unstructured":"Nguyen, T.V., and Bonilla, E.V. (2014, January 23\u201327). Collaborative multi-output Gaussian processes. Proceedings of the UAI\u201914: Thirtieth Conference on Uncertainty in Artificial Intelligence, Citeseer, Quebec City, QC, Canada."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Carron, A., Todescato, M., Carli, R., Schenato, L., and Pillonetto, G. (2015, January 15\u201317). Multi-agents adaptive estimation and coverage control using Gaussian regression. Proceedings of the 2015 European Control Conference (ECC), Linz, Austria.","DOI":"10.1109\/ECC.2015.7330912"},{"key":"ref_65","unstructured":"Mallasto, A., and Feragen, A. (2017, January 4\u20139). Learning from uncertain curves: The 2-wasserstein metric for gaussian processes. Proceedings of the 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K. (2006). Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_67","first-page":"20","article-title":"Nonlinear Systems: International Edition","volume":"53","author":"Khalil","year":"2002","journal-title":"Bull. Am. Acad. Arts Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4154","DOI":"10.1109\/TAC.2019.2958840","article-title":"Feedback linearization based on Gaussian processes with event triggered online learning","volume":"65","author":"Umlauft","year":"2020","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10589-006-6446-0","article-title":"Gradient methods with adaptive step-sizes","volume":"35","author":"Zhou","year":"2006","journal-title":"Comput. Optim. Appl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.procs.2020.11.033","article-title":"Mathematical modeling of the dynamics of 3-DOF robot-manipulator with software control","volume":"178","author":"Ivanov","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_71","unstructured":"Abdolhosseini, M. (2012). Model Predictive Control of an Unmanned Quadrotor Helicopter: Theory and Flight Tests. [Ph.D. Thesis, Concordia University]."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.arcontrol.2004.05.001","article-title":"Efficient nonlinear model predictive control algorithms","volume":"28","author":"Cannon","year":"2004","journal-title":"Annu. Rev. Control."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.automatica.2019.01.023","article-title":"Stable Gaussian process based tracking control of Euler-Lagrange systems","volume":"103","author":"Beckers","year":"2019","journal-title":"Automatica"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3250","DOI":"10.1109\/TIT.2011.2182033","article-title":"Information-theoretic regret bounds for Gaussian process optimization in the bandit setting","volume":"58","author":"Srinivas","year":"2012","journal-title":"IEEE Trans. Inf. 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