Bibliography on Gaussian Process Models in Dynamic Systems Modelling


1999

Roderick Murray-Smith, Tor Arne Johansen, Robert Shorten.
On transient dynamics, off-equilibrium behaviour and identification in blended multiple model structures.
In Proceedings of the European Control Conference (ECC99), Pages BA–14, 1999.


2000

William E. Leithead, Douglas J. Leith, Roderick Murray-Smith.
A Gaussian Process prior/velocity-based Framework for Nonlinear Modelling and Control.
In Irish Signals and Systems Conference, 2000.

Douglas J. Leith, Roderick Murray-Smith, William E. Leithead.
Nonlinear structure identification: A non-parametric/velocity-based approach.
In Proceedings of the UKACC Control Conference, 2000.


2001

Vladan Babovic, Maarten Keijzer.
A Gaussian Process Model Applied to Prediction of the Water Levels in Venice Lagoon.
In Proceedings Of The XXIX Congress Of International Association For Hydraulic Research, Pages 509–513, 2001.

Roderick Murray-Smith, Agathe Girard.
Gaussian Process priors with ARMA noise models.
In Irish Signals and Systems Conference, Maynooth, Ireland, Pages 147–152, 2001.


2002

Blaž Banko, Juš Kocijan.
Uporaba Gaussovih procesov za identifikacijo nelinearnih sistemov.
In Zbornik enajste elektrotehniške in računalniške konference (ERK 2002), Volume A, Pages 323–326, 2002.

Agathe Girard, Carl Edward Rasmussen, Roderick Murray-Smith.
Gaussian process priors with uncertain inputs: Multiple-step-ahead prediction.
Technical Report DCS TR-2002-119, University of Glasgow, Glasgow, 2002.

Gregor Gregorčič, Gordon Lightbody.
Gaussian Processes for Modelling of Dynamic Non-linear Systems.
In Proceedings of the Irish Signals and Systems Conference, Pages 141–147, 2002.

Gregor Gregorčič, Gordon Lightbody.
Gaussian Process for Internal Model Control.
In Proceedings of 3rd International PhD Workshop on Advances in Supervision and Control Systems, A Young Generation Viewpoint, Pages 39–46, 2002.

Juš Kocijan.
Gaussian Process Model Based Predictive Control.
Technical Report DP-8710, Institute Jožef Stefan, Ljubljana, 2002.

Juš Kocijan, Bojan Likar, Blaž Banko, Agathe Girard, Roderick Murray-Smith, Carl Edward Rasmussen.
Identification of pH neutralization process with neural networks and Gaussian process model: MAC project.
Technical Report DP-8575, Institute Jožef Stefan, Ljubljana, 2002.

Douglas J. Leith.
On identifying nonlinear dynamic structure from time series data.
Proceedings of Workshop on Modern Methods for Data Intensive Modelling, 2002.

Douglas J. Leith, William E. Leithead, Ercan Solak, Roderick Murray-Smith.
Divide and conquer identification using Gaussian processes.
In Proceedings of IEE Workshop on Nonlinear and Non-Gaussian signal processing (N2SP), 2002.

Roderick Murray-Smith, Daniel Sbarbaro.
Nonlinear adaptive control using nonparametric Gaussian process prior models.
In Proceedings of IFAC 15th World Congress, 2002.

Roderick Murray-Smith, Robert Shorten, Douglas J. Leith.
Nonparametric models of dynamic systems.
In Proceedings of IEE Workshop on Nonlinear and Non-Gaussian signal processing (N2SP), 2002.


2003

Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero-Candela, Roderick Murray-Smith.
Bayesian regression and Gaussian process priors with uncertain inputs - application to multiple-step ahead time series forecasting.
In Advances in Neural Information Processing Systems conference, Volume 15, Pages 529–536, 2003.

Gary Gray, Roderick Murray-Smith, Keith Russell Thompson, David James Murray-Smith.
Tutorial example of Gaussian process prior modelling applied to twin-tank system.
Technical Report DCS TR-2003-151, University of Glasgow, Glasgow, 2003.

Gregor Gregorčič, Gordon Lightbody.
Internal model control based on Gaussian process prior model.
In Proceedings of the 2003 American Control Conference (ACC 2003), Pages 4981–4986, 2003.

Gregor Gregorčič, Gordon Lightbody.
From multiple model networks to the Gaussian processes prior model.
In Proceedings of IFAC ICONS conference, Pages 149–154, 2003.

Gregor Gregorčič, Gordon Lightbody.
An affine Gaussian process approach for nonlinear system identification.
Systems Science Journal, Volume 29, Pages 47–63, 2003.

Jostein Hansen.
Using Gaussian processes as a modelling tool in control systems.
Technical Report DCS TR-2003, University of Glasgow, Glasgow, 2003.

Juš Kocijan, Blaž Banko, Bojan Likar, Agathe Girard, Roderick Murray-Smith, Carl Edward Rasmussen.
A case based comparison of identification with neural network and Gaussian process models.
In Proceedings of IFAC ICONS Conference, Volume 1, Pages 137–142, 2003.

Juš Kocijan, Agathe Girard, Blaž Banko, Roderick Murray-Smith.
Dynamic systems identification with Gaussian processes.
In Proceedings of 4th IMACS Symposium on Mathematical Modelling (MathMod), Pages 776–784, 2003.

Juš Kocijan, Agathe Girard, Douglas J. Leith.
Incorporating linear local models in Gaussian process model.
Technical Report DP-8895, Institut Jožef Stefan, Ljubljana, 2003.

Juš Kocijan, Roderick Murray-Smith, Carl Edward Rasmussen, Bojan Likar.
Predictive control with Gaussian process models.
In The IEEE Region 8 EUROCON: computer as a tool, Volume A, Pages 352–356, 2003.

Douglas J. Leith, William E. Leithead, Roderick Murray-Smith.
Nonlinear structure identification with application to Wiener-Hammerstein systems.
In Proceedings of 13th IFAC Symposium on System Identification, 2003.

William E. Leithead, Ercan Solak, Douglas J. Leith.
Direct identification of nonlinear structure using Gaussian process prior models.
In Proceedings of European Control Conference (ECC 2003), Cambridge, UK, 2003.

Roderick Murray-Smith, Daniel Sbarbaro, Carl Edward Rasmussen, Agathe Girard.
Adaptive, cautious, predictive control with Gaussian process priors.
In Proceedings of 13th IFAC Symposium on System Identification, 2003.

Joaquin Quiñonero-Candela, Agathe Girard.
Prediction at uncertain input for Gaussian processes and relevance vector machines - Application to multiple-step ahead time-series forecasting.
Technical Report IMM-2003-18, Technical University Denmark, Informatics and Mathematical Modelling, Kongens Lyngby, 2003.

Joaquin Quiñonero-Candela, Agathe Girard, Jan Larsen, Carl Edward Rasmussen.
Propagation of uncertainty in Bayesian kernel models - Application to multiple-step ahead forecasting.
In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Volume 2, Pages 701–704, 2003.

Daniel Sbarbaro, Roderick Murray-Smith.
Self-tuning control of nonlinear systems using Gaussian process prior models.
Technical Report DCS TR-2003-143, University of Glasgow, Glasgow, 2003.

Ercan Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl Edward Rasmussen.
Derivative observations in Gaussian process models of dynamic systems.
In Advances in Neural Information Processing Systems conference, Volume 15, Pages 529–536, 2003.


2004

Juš Kocijan, Douglas J. Leith.
Derivative Observations Used in Predictive Control.
In Proceedings of IEEE Melecon, Volume 1, Pages 379–382, 2004.

Juš Kocijan, Roderick Murray-Smith, Carl Edward Rasmussen, Agathe Girard.
Gaussian Process Model Based Predictive Control.
In Proceedings of 4th American Control Conference (ACC 2004), Pages 2214–2218, 2004.

Douglas J. Leith, Martin Heidl, John V. Ringwood.
Gaussian Process Prior Models for Electrical Load Forecasting.
In International Conference on Probabilistic Methods Applied to Power Systems, Pages 112–117, 2004.

Carl Edward Rasmussen, Malte Kuss.
Gaussian Processes in Reinforcement Learning.
In Advances in Neural Information Processing Systems conference, Volume 16, Pages 751–759, 2004.

Daniel Sbarbaro, Roderick Murray-Smith, Arturo Valdes.
Multivariable generalized minimum variance control based on artificial neural networks and Gaussian process models.
In International Symposium on Neural Networks, 2004.


2005

Kristjan Ažman.
Incorporating prior knowledge into Gaussian process model.
In Proceedings of 6th International PhD Workshop on Systems and Control - A Young Generation Viewpoint, Volume A, Pages 253–256, 2005.

Kristjan Ažman, Juš Kocijan.
An example of Gaussian process model identification.
In Proceedings of 28th International conference MIPRO, CIS - Inteligent Systems, Pages 79–84, 2005.

Kristjan Ažman, Juš Kocijan.
Identifikacija dinamičnega sistema s histerezo z modelom na osnovi Gaussovih procesov.
In Zbornik štirinajste elektrotehniške in računalniške konference (ERK 2005), Volume A, Pages 253–256, 2005.

Kristjan Ažman, Juš Kocijan.
Comprising prior knowledge in dynamic Gaussian process models.
In Proceedings of the International Conference on Computer Systems and Technologies (CompSysTech), Pages IIIB.2–1—-IIIB.2–6, 2005.

Yaakov Engel, Shie Mannor, Ron Meir.
Reinforcement learning with Gaussian processes.
In Proceedings of the 22nd international conference on Machine learning, Pages 201–208, 2005.

Yaakov Engel, Peter Szabo, Dmitry Volkinshtein.
Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods.
In Advances in Neural Information Processing Systems, Volume 18, Pages 347–354, 2005.

Agathe Girard, Roderick Murray-Smith.
Gaussian processes: Prediction at a noisy input and application to iterative multiple-step ahead forecasting of time-series.
Lecture Notes in Computer Science, 2005.

Gregor Gregorčič, Gordon Lightbody.
Gaussian Process Approaches to Nonlinear Modelling for Control.
Intelligent Control Systems Using Computational Intelligence Techniques, IEE Intelligent Control Series, 2005.

Jostein Hansen, Roderick Murray-Smith, Tor Arne Johansen.
Nonparametric identification of linearizations and uncertainty using Gaussian process models—application to robust wheel slip control.
In Joint 44th IEEE conference on decision and control and European control conference (CDC-ECC), Pages 7994–7999, 2005.

Juš Kocijan, Agathe Girard.
Incorporating linear local models in Gaussian process model.
In Proceedings of IFAC 16th World Congress, 2005.

Juš Kocijan, Agathe Girard, Blaž Banko, Roderick Murray-Smith.
Dynamic systems identification with Gaussian processes.
Mathematical and Computer Modelling of Dynamical Systems, Volume 11, Pages 411–424, 2005.

Juš Kocijan, Roderick Murray-Smith.
Nonlinear predictive control with Gaussian process model.
Lecture Notes in Computer Science, 2005.

William E. Leithead.
Identification of Nonlinear Dynamic Systems by Combining Equilibrium and Off-Equilibrium Information.
In Proceedings of International Conference on Industrial Electronics and Control Applications (ICIECA), 2005.

William E. Leithead, Keith Kian Seng Neo, Douglas J. Leith.
Gaussian regression based on models with two stochastic processes.
In Proceedings of IFAC 16th World Congress, Prague, Czech Republic, 2005.

William E. Leithead, Yunong Zhang, Douglas J. Leith.
Efficient hyperparameter estimation of Gaussian process regression based on quasi-Newton BFGS update and power series approximation.
In Proceedings of IFAC 16th World Congress, 2005.

William E. Leithead, Yunong Zhang, Douglas J. Leith.
Time-series Gaussian process regression based on Toeplitz computation of O(N2) operations and O(N) level storage.
In Joint 44th IEEE conference on decision and control and European control conference (CDC-ECC), 2005.

William E. Leithead, Yunong Zhang, Keith Kian Seng Neo.
Wind turbine rotor acceleration: identification using Gaussian regression.
In Proceedings of International conference on informatics in control automation and robotics (ICINCO), 2005.

Hiroyuki Mori, Masatarou Ohmi.
Probabilistic short-term load forecasting with Gaussian processes.
In Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, Pages 452–457, 2005.

Roderick Murray-Smith, Barak A. Pearlmutter.
Transformations of Gaussian process priors.
Lecture Notes in Artificial Intelligence, 2005.

Rainer Palm.
Multi-step-ahead prediction with Gaussian Processes and TS-Fuzzy Models.
In Proceedings of 14th IEEE International Conference on Fuzzy Systems, Pages 945–950, 2005.

Daniel Sbarbaro, Roderick Murray-Smith.
An adaptive nonparametric controller for a class of nonminimum phase non-linear system.
In Proceedings of IFAC 16th World Congress, 2005.

Daniel Sbarbaro, Roderick Murray-Smith.
Self-tuning control of nonlinear systems using Gaussian process prior models.
Lecture Notes in Computer Science, 2005.

Jian Qing Shi, Roderick Murray-Smith, D. Mike Titterington.
Hierarchical Gaussian process mixtures for regression.
Statistics and Computing, Volume 15, Pages 31–41, 2005.

Sethu Vijayakumar, Aaron D'souza, Stefan Schaal.
Incremental Online Learning in High Dimensions.
Neural Computation, Volume 17, Pages 2602–2634, 2005.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.
Gaussian Process Dynamical Models.
Advances in Neural Information Processing Systems, Volume 18, Pages 1441–1448, 2005.

Zhi-hua Xiong, Hai-bin Yang, Yun-feng Wu, Hui-he Shao.
Sparse GP-based soft sensor applied to the power plant.
Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc. Electr. Eng.), Volume 25, Pages 130–133, 2005.

Zhi-hua Xiong, Wei-qing Zhang, Yu Zhao, Hui-he Shao.
Thermal parameter soft sensor based on the mixture of Gaussian processes.
Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc. Electr. Eng.), Volume 25, Pages 30–33, 2005.

Yunong Zhang, William E. Leithead.
Exploiting Hessian matrix and trust region algorithm in hyperparameters estimation of Gaussian process.
Applied Mathematics and Computation, Volume 171, Pages 1264–1281, 2005.


2006

Kristjan Ažman, Juš Kocijan.
Gaussian process model validation: biotechnological case studies.
In Proceedings of the 5th Vienna Symposium on Mathematical Modeling (MathMod), 2006.

Kristjan Ažman, Juš Kocijan.
Identifikacija dinamičnega sistema z znanim modelom šuma z modelom na osnovi Gaussovih procesov.
In Zbornik petnajste elektrotehniške in računalniške konference (ERK), Pages 289–292, 2006.

Kristjan Ažman, Juš Kocijan.
An application of Gaussian process models for control design.
In UKACC International Control Conference, 2006.

Boštjan Grašič, Primož Mlakar, Marija Zlata Božnar.
Ozone prediction based on neural networks and Gaussian processes.
Nuovo Cimento della Societa Italiana di Fisica, Sect. C, Volume 29, Pages 651–662, 2006.

David B. Grimes, Rawichote Chalodhorn, Rajesh P. N. Rao.
Dynamic Imitation in a Humanoid Robot through Nonparametric Probabilistic Inference.
In Robotics: Science and Systems, 2006.

Đani Juričić, Juš Kocijan.
Fault detection based on Gaussian process model.
In Proceedings of the 5th Vienna Symposium on Mathematical Modeling (MathMod), 2006.

Douglas J. Leith, Roderick Murray-Smith, William E. Leithead.
Inference of disjoint linear and nonlinear subdomains of a nonlinear mapping.
Automatica, Volume 42, Pages 849–858, 2006.

Kooksang Moon, Vladimir Pavlović.
Impact of Dynamics on Subspace Embedding and Tracking of Sequences.
In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, Pages 198–205, 2006.

Keith Kian Seng Neo, William E. Leithead, Yunong Zhang.
Multi-frequency scale Gaussian regression for noisy time-series data.
In UKACC International Control Conference, 2006.

Masatarou Ohmi, Hiroyuki Mori.
A Gaussian processes technique for short-term load forecasting with considerations of uncertainty.
IEEJ Transactions on Power and Energy, Volume 126, Pages 202–208, 2006.

Carl Edward Rasmussen, Christopher K. I. Williams.
Gaussian Processes for Machine Learning.
MIT Press, 2006.

Keith Russell Thompson, David James Murray-Smith.
Implementation of Gaussian process models for nonlinear system identification.
In Proceedings of the 5th Vienna Symposium on Mathematical Modeling (MathMod), 2006.

Raquel Urtasun, David J. Fleet, Pascal Fua.
3D people tracking with Gaussian process dynamical models.
In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Volume 1, Pages 238–245, 2006.


2007

Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor.
Gaussian Process Approximations of Stochastic Differential Equations.
In Journal of Machine Learning Research: Workshop and Conference Proceedings, Volume 1, Pages 1–16, 2007.

Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor.
Variational Inference for Diffusion Processes.
Advances in Neural Information Processing Systems, 2007.

Kristjan Ažman, Juš Kocijan.
Application of Gaussian processes for black-box modelling of biosystems.
ISA transactions, Volume 46, Pages 443–457, 2007.

S. Calinon, F. Guenter, A Billard.
On Learning, Representing, and Generalizing a Task in a Humanoid Robot.
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, Volume 37, Pages 286–298, 2007.

Marc P. Deisenroth, Florian Weissel, Toshiyuki Ohtsuka, Uwe D. Hanebeck.
Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces.
In In Proceedings of the European Control Conference (ECC 2007, 2007.

Luka Eciolaza, M. Alkarouri, N. D. Lawrence, V. Kadirkamanathan, P. J. Fleming.
Gaussian Process Latent Variable Models for Fault Detection.
In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007), Pages 287–292, 2007.

Stephen Faul, Gregor Gregorčič, Geraldine Boylan, William Marnane, Gordon Lightbody, Sean Connolly.
Gaussian process modeling of EEG for the detection of neonatal seizures.
IEEE Transactions on Biomedical Engineering, Volume 54, Pages 2151–2162, 2007.

Alexandra Grancharova, Juš Kocijan.
Stochastic predictive control of a thermoelectric power plant.
In Proceedings of the International Conference Automatics and Informatics'07, Pages I-13–I-16, 2007.

Alexandra Grancharova, Juš Kocijan, Tor Arne Johansen.
Explicit stochastic nonlinear predictive control based on Gaussian process models.
In Proceedings of European control conference (ECC), Pages 2340–2347, 2007.

Gregor Gregorčič, Gordon Lightbody.
Local model identification with Gaussian processes.
IEEE Transactions on neural networks, Volume 18, Pages 1404–1423, 2007.

Tomohiro Hachino, Visakan Kadirkamanathan.
Time series forecasting using multiple Gaussian process prior model.
In IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Pages 604–609, 2007.

Jonathan Ko, Daniel J. Klein, Dieter Fox, Dirk Haehnel.
Gaussian Processes and Reinforcement Learning for Identification and Control of an Autonomous Blimp.
In Proceedings of the International Conference on Robotics and Automation, Pages 742–747, 2007.

Jonathan Ko, Daniel J. Klein, Dieter Fox, Dirk Haehnel.
GP-UKF: Unscented Kalman filters with Gaussian process prediction and observation models.
In Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, Pages 1901–1907, 2007.

Juš Kocijan.
Identifikacija nelinearnih sistemov z Gaussovimi procesi.
Modeliranje dinamičnih sistemov z umetnimi nevronskimi mrežami in sorodnimi metodami, Pages 73–86, 2007, (in Slovene).

Juš Kocijan, Kristjan Ažman.
Gaussian process model identification: a process engineering case study.
In Proceedings of the 16th International Conference on Systems Science, Volume 1, Pages 418–427, 2007.

Juš Kocijan, Kristjan Ažman, Alexandra Grancharova.
The concept for Gaussian process model based system identification toolbox.
In Proceedings of the InternationalConference on Computer Systems and Technologies (CompSysTech), Pages IIIA.23–1—-IIIA.23–6, 2007.

Juš Kocijan, Bojan Likar.
Gas-liquid separator modelling and simulation with Gaussian-process models.
In Proceedings of the 6th EUROSIM Congress on Modelling and Simulation (EUROSIM), 2007.

William E. Leithead, Yunong Zhang.
O(N-2)-operation approximation of covariance matrix inverse in Gaussian process regression based on quasi-Newton BFGS method.
Communications in Statistics-Simulation and Computation, Volume 36, Pages 367–380, 2007.

Bojan Likar, Juš Kocijan.
Predictive control of a gas-liquid separation plant based on a Gaussian process model.
Computers and chemical engineering, Volume 31, Pages 142–152, 2007.

Marta Neve, Giuseppe De Nicolao, Laura Marchesi.
Nonparametric identification of population models via Gaussian processes.
Automatica, Volume 43, Pages 1134–1144, 2007.

Rainer Palm.
Multiple-step-ahead prediction in control systems with Gaussian process models and TS-fuzzy models.
Engineering Applications of Artificial Intelligence, Volume 20, Pages 1023–1035, 2007.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.
Multifactor Gaussian process models for style-content separation.
In Proceedings of the 24th international conference on Machine learning, 2007.

H. Wu, F. Sun.
Adaptive Kriging control of discrete-time nonlinear systems.
IET Control Theory & Applications, Volume 1, Pages 646–656, 2007.

Yunong Zhang, William E. Leithead.
Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression.
Journal of Statistical Computation and Simulation, Volume 77, Pages 329–348, 2007.


2008

Kristjan Ažman, Juš Kocijan.
Non-linear model predictive control for models with local information and uncertainties.
Transactions of the Institute of Measurement and Control, Volume 30, 2008.

B. Calderhead, M. Girolami, N. D. Lawrence.
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes.
In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference, Volume 1, Pages 217–224, 2008.

Kian M. Chai, Christopher Williams, Stefan Klanke, Sethu Vijayakumar.
Multi-task Gaussian Process Learning of Robot Inverse Dynamics.
Advances in Neural Information Processing Systems 21, 2008.

Alessandro Chiuso, Gianluigi Pillonetto, Giuseppe De Nicolao.
Subspace identification using predictor estimation via Gaussian regression.
In Proceedings of the IEEE Conference on Decision and Control, 2008.

Jongeun Choi, Joonho Lee, Songhwai Oh.
Swarm intelligence for achieving the global maximum using spatio-temporal Gaussian processes.
In Proceedings of American Control Conference (ACC), Pages 135–140, 2008.

Jongeun Choi, Joonho Lee, Songhwai Oh.
Biologically-inspired navigation strategies for swarm intelligence using spatial Gaussian processes.
In Proceedings of IFAC 17th World Congress, Pages 593–598, 2008.

Giuseppe De Nicolao, Gianluigi Pillonetto.
A new kernel-based approach for system identification.
In Proceedings of American Control Conference (ACC), Pages 4510–4516, 2008.

Marc Peter Deisenroth, Jan Peters, Carl Edward Rasmussen.
Approximate dynamic programming with Gaussian processes.
In Proceedings of American Control Conference (ACC), Pages 4480–4485, 2008.

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters.
Model-Based Reinforcement Learning with Continuous States and Actions.
In Proceedings of the European Symposium on Artificial Neural Networks (ESANN), Pages 19–24, 2008.

Carl Henrik Ek, Philip H.S. Torr, Neil D. Lawrence.
Gaussian Process Latent Variable Models for Human Pose Estimation.
Machine Learning for Multimodal Interaction, Lecture Notes in Computer Science, 2008.

Alexandra Grancharova, Juš Kocijan, Tor Arne Johansen.
Explicit stochastic predictive control of combustion plants based on Gaussian process models.
Automatica, Volume 44, Pages 1621–1631, 2008.

Gregor Gregorčič, Gordon Lightbody.
Nonlinear system identification: From multiple-model networks to Gaussian processes.
Engineering Applications of Artificial Intelligence, Volume 21, Pages 1035–1055, 2008.

Tomohiro Hachino, Hitoshi Takata.
Identification of continuous-time nonlinear systems by using a Gaussian process model.
IEEJ Transactions on Electrical and Electronic Engineering, Volume 3, Pages 620–628, 2008.

Jonathan Ko, Dieter Fox.
GP-Bayes Filters: Bayesian filtering using Gaussian process prediction and observation models.
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pages 3471–3476, 2008.

Juš Kocijan.
Gaussian Process Models for Systems Identification.
In Proceedings of 9th Intetnational PhD Workshop on Systems and Control: young generation viewpoint, 2008.

Juš Kocijan, Kristjan Ažman.
Gaussian process model identification: a process engineering case study.
Systems Science Journal, Volume 34, Pages 31–38, 2008.

Juš Kocijan, Bojan Likar.
Gas-liquid separator modelling and simulation with Gaussian-process models.
Simulation Modelling Practice and Theory, Volume 16, Pages 910–922, 2008.

Duy Nguyen-Tuong, Jan Peters.
Learning Robot Dynamics for Computed Torque Control Using Local Gaussian Processes Regression.
In Symposium on Learning and Adaptive Behaviors for Robotic Systems, Pages 59–64, 2008.

Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Bernhard Schölkopf.
Learning Inverse Dynamics: a Comparison.
In Proceedings of the European Symposium on Artificial Neural Networks (ESANN), Pages 13–18, 2008.

Duy Nguyen-Tuong, Matthias Seeger, Jan Peters.
Computed Torque Control with Nonparametric Regression Models.
In Proceedings of the American Control Conference (ACC), Pages 212–217, 2008.

Gianluigi Pillonetto, Alessandro Chiuso, Giuseppe De Nicolao.
Predictor estimation via Gaussian regression.
In Proceedings of the IEEE Conference on Decision and Control, 2008.

Luc Pronzato.
Optimal experimental design and some related control problems.
Automatica, Volume 44, Pages 303–325, 2008.

Carl Edward Rasmussen, Marc Peter Deisenroth.
Probabilistic Inference for Fast Learning in Control.
Recent Advances in Reinforcement Learning, Lecture Notes on Computer Science, 2008.

Kyle Schmitt, Justin Madsen, Mihai Anitescu, Dan Negrut.
A Gaussian process based approach for handling uncertainty in vehicle dynamics simulation.
International Mechanical Engineering Congress and Exposition (IMECE), 2008.

Fernando di Sciascio, Adriana N. Amicarelli.
Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression.
Computers and Chemical Engineering, Volume 32, Pages 3264–3273, 2008.

Sylvain Vinet, Emmanuel Vazquez.
Black-box identification and simulation of continuous-time nonlinear systems with random processes.
In Proceedings of the IFAC 17th World Congress, Pages 14391–14396, 2008.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.
Gaussian Process Dynamical Models for Human Motion.
IEEE transactions on pattern analysis and machine intelligence, Volume 30, Pages 283–298, 2008.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.
Erratum: Gaussian process dynamical models for human motion.
IEEE transactions on pattern analysis and machine intelligence, Volume 30, Pages 1118, 2008.

Jin Yuan, Kesheng Wang, Tao Yu, Minglun Fang.
Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression.
International Journal of Machine Tools and Manufacture, Volume 48, Pages 47–60, 2008.


2009

Ali Abusnina, Daniel Kudenko.
Adaptive Soft Sensor based on Moving Gaussian Process Window.
International Conference on Mechatronics Automation (ICMTMA), 2009.

Mauricio A. Álvarez, David Luengo, Neil D. Lawrence.
Latent Force Models.
Journal of Machine Learning Research - Proceedings Track, Volume 5, Pages 9–16, 2009.

Kristjan Ažman, Juš Kocijan.
Fixed-structure Gaussian process model.
International Journal of Systems Science, Volume 40, Pages 1253–1262, 2009.

Jixu Chen, Minyoung Kim, Yu Wang, Qiang Ji.
Switching Gaussian Process Dynamic Models for simultaneous composite motion tracking and recognition.
In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Pages 2655–2662, 2009.

S. Conti, J. P. Gosling, J. E. Oakley, A. O'Hagan.
Gaussian process emulation of dynamic computer codes.
Biometrika, Volume 3, Pages 663–676, 2009.

Patrick Dallaire, Camille Besse, Brahim Chaib-Draa.
Learning Gaussian process models from uncertain data.
Neural Information Processing, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009.

Patrick Dallaire, Camille Besse, Stéphane Ross, Brahim Chaib-Draa.
Bayesian reinforcement learning in continuous POMDPs with Gaussian processes.
In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, Pages 2604–2609, 2009.

Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hannebeck.
Analytic moment-based Gaussian process filtering.
In Proceedings of the 26th Annual International Conference on Machine Learning, Pages 225–232, 2009.

Marc Peter Deisenroth, Carl Edward Rasmussen.
Bayesian Inference for Efficient Learning in Control.
In Proceedings of Multidisciplinary Symposium on Reinforcement Learning (MSRL), 2009.

Marc Peter Deisenroth, Carl Edward Rasmussen.
Bayesian Inference for Efficient Learning in Control.
In Proceedings of the 10th International PhD Workshop on Systems and Control, a Young Generation Viewpoint, 2009.

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters.
Gaussian process dynamic programming.
Neurocomputing, Volume 72, Pages 1508–1524, 2009.

Gregor Gregorčič, Gordon Lightbody.
Gaussian process approach for modelling of nonlinear systems.
Engineering Applications of Artificial Intelligence, Volume 22, Pages 522–533, 2009.

Leslie Ikemoto, Okan Arikan, David A. Forsythe.
Generalizing motion edits with Gaussian processes.
ACM Transactions on Graphics, Volume 28, Pages 1–12, 2009.

Johnsen Kho, Alex Rogers, Nicholas R. Jennings.
Decentralised Control of Adaptive Sampling in Wireless Sensor Networks.
ACM Transactions on Sensor Networks, Volume 5, Pages 1–35, 2009.

Jonathan Ko, Dieter Fox.
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models.
Autonomous Robots, Volume 27, Pages 75–90, 2009.

Jonathan Ko, Dieter Fox.
Learning GP-BayesFilters via Gaussian process latent variable models.
Robotics: science and systems, 2009.

Juš Kocijan, Kristjan Ažman.
Application of varying parameters modelling with Gaussian processes.
In 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing (ICONS), 2009.

Subhasish Mohanty, Santanu Das, Aditi Chattopadhyay, Pedro Peralta.
Gaussian Process Time Series Model for Life Prognosis of Metallic Structures.
Journal of Intelligent Material Systems and Structures, Volume 20, Pages 887–896, 2009.

Duy Nguyen-Tuong, Matthias Seeger, Jan Peters.
Model learning with local Gaussian process regression.
Advanced Robotics, Volume 23, Pages 2015–2034, 2009.

Jerome Le Ny, George J. Pappas.
On trajectory optimization for active sensing in Gaussian process models.
In Proceedings of the IEEE Conference on Decision and Control, Pages 6286–6292, 2009.

Gianluigi Pillonetto, Alessandro Chiuso.
A Bayesian learning approach to linear system identification with missing data.
In Proceedings of the IEEE Conference on Decision and Control, Pages 4698–4703, 2009.

Axel Rottmann, Wolfram Burgard.
Adaptive autonomous control using online value iteration with Gaussian processes.
In Proceedings - IEEE International Conference on Robotics and Automation, Pages 2106–2111, 2009.

Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen.
System Identification in Gaussian Process Dynamical Systems.
In Nonparametric Bayes Workshop at NIPS, 2009.

Dit-Yan Yeung, Yu Zhang.
Learning Inverse Dynamics by Gaussian process Regression under the Multi-Task Learning Framework.
The Path to Autonomous Robots, 2009.

Guanling Zhou, Nanping Dong, Yuping Wang.
Non-Linear Dynamic Texture Analysis and Synthesis Using Constrained Gaussian Process Latent Variable Model.
In Proceedings of the Pacific-Asia Conference on Circuits, Communications and System (PACCS), Pages 27–30, 2009.

Wen-yun Zhou, Qaun Liu.
A Gaussian Processes Reinforcement Learning Method in Large Discrete State Spaces.
In Proceedings of International Conference on Advanced Computer Control (ICACC), Pages 589–593, 2009.


2010

Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence.
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels.
Journal of Machine Learning Research - Proceedings Track, Volume 9, Pages 25–32, 2010.

Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence.
Switched Latent Force Models for Movement Segmentation.
In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada, Pages 55–63, 2010.

Cédric Archambeau, Manfred Opper.
Approximate inference for continuous-time Markov processes.
Inference and Learning in Dynamic Models, 2010.

Liefeng Bo, Cristian Sminchisescu.
Twin Gaussian processes for structured prediction.
International Journal of Computer Vision, Volume 87, Pages 28–52, 2010.

Satish T. S. Bukkapatnam, Changqing Cheng.
Forecasting the evolution of nonlinear and nonstationary systems using recurrence-based local Gaussian process models.
Physical Review E, Volume 82, 2010.

Salil Deena, Shaobo Hou, Aphrodite Galata.
Visual Speech Synthesis by Modelling Coarticulation Dynamics using a Non-Parametric Switching State-Space Model.
In International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2010), 2010.

Marc Peter Deisenroth, Henrik Ohlsson.
A Probabilistic Perspective on Gaussian Filtering and Smoothing.
ArXiv e-prints, 2010.

Marc Peter Deisenroth, Carl Edward Rasmussen.
A Practical and Conceptual Framework for Learning in Control.
Technical Report UW-CSE-10-06-01, Department of Computer Science & Engineering, University of Washington, Seattle, 2010.

Marc Peter Deisenroth, Carl Edward Rasmussen.
Reducing Model Bias in Reinforcement Learning.
In Learning and Planning from Batch Time Series Data, 2010.

Dragoljub Gagi Drmanac, Brendon Bolin, Li-C. Wang.
A Non-Parametric Approach to Behavioral Device Modeling.
In 1th International Symposium on Quality of Electronic Design (ISQED), Pages 284–290, 2010.

Denis Forte, Aleš Ude, Andrej Kos.
Robot learning by Gaussian process regression.
In Proceedings of the 19th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2010), Pages 303–308, 2010.

Alexandra Grancharova, Juš Kocijan, Alexander Krastev, Hristina Hristova.
High-order Gaussian process models for prediciton of ozone concentration in the air.
In Proceedings of the 7th EUROSIM Congress on Modelling and Simulation EUROSIM 2010, Volume 2, Pages 8, 2010.

Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert.
Non-Stationary Space-Carving Kernels for Accurate Rough Terrain Estimation.
International Journal of Robotics Research, Volume 29, Pages 981–996, 2010.

Andres Felipe Hernandez, Martha Grover.
Stochastic dynamic predictions using Gaussian process models for nanoparticle synthesis.
Computers and Chemical Engineering, Volume 34, Pages 1953–1961, 2010.

Antti Honkela, Charles Girardot, E. Hilary Gustafson, Ya-Hsin Liu, E. M. Furlong Furlong, Neil D. Lawrence, Magnus Rattray.
Model-based method for transcription factor target identification with limited data.
Proceedings of the National Academy of Sciences of the United States of America, Volume 107, Pages 7793–7798, 2010.

X. Jiang, B. Donga, L. Xie, L. Sweeney.
Adaptive Gaussian Process for Short-Term Wind Speed Forecasting.
In Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence, Pages 661–666, 2010.

T. Jung, P. Stone.
Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration.
Lecture Notes in Computer Science, Volume 6321, Pages 601–616, 2010.

Juš Kocijan, Alexandra Grancharova.
Gaussian process modelling case study with multiple outputs.
Comptes Rendus de l Academie Bulgare des Sciences, Volume 36, Pages 601–607, 2010.

Juš Kocijan, Jan Prikryl.
Soft Sensor for Faulty Measurements Detection and Reconstruction in Urban Traffic.
In Proceedings 15th IEEE Mediterranian Electromechanical Conference (MELECON), Pages 172–177, 2010.

Peng Li, Shen-min Song, Xing-lin Chen, Guang-ren Duan.
Square root unscented Kalman filter incorporating Gaussian process regression.
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, Volume 32, Pages 1281–1285, 2010.

João Lourenço, Paulo J. Santos.
Short term load forecasting using Gaussian process models.
Technical Report ISSN: 1645-2631, Issue 5, Instituto de Engenharia de Sistemas e Computadores de Coimbra Institute of Systems Engineering and Computers INESC, 2010.

Bojan Musizza, Dejan Petelin, Juš Kocijan.
Accelerated learning of Gaussian process models.
In Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, 2010.

J. C. Nascimento, J. G. Silva.
Manifold Learning for Object Tracking with Multiple Motion Dynamics.
Lecture Notes in Computer Science, Volume 6313, Pages 172–185, 2010.

Duy Nguyen-Tuong, Jan Peters.
Using Model Knowledge for Learning Inverse Dynamics.
In Proceedings of IEEE International Conference on Robotics and Automation, Pages 2677–2682, 2010.

Duy Nguyen-Tuong, Matthias Seeger, Jan Peters.
Real-time local GP model learning.
From Motor Learning to Interaction Learning in Robots, Volume 264, Pages 193–207, 2010.

Michael A. Osborne, Roman Garnett, Stephen J. Roberts.
Active Data Selection for Sensor Networks with Faults and Changepoints.
In Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications, Pages 533–540, 2010.

Veli Peltola, Antti Honkela.
Variational inference and learning for non-linear state-space models with state-dependent observation noise.
In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010, Pages 190–195, 2010.

Dejan Petelin, Juš Kocijan.
Application of on-line Gaussian process models for pressure signal.
In Proceedings of the 11th International PhD Workshop on Systems and Control, September 1-3, 2010, Veszprém, Hungary : a young generation viewpoint, Pages 39–44, 2010.

Gianluigi Pillonetto, Giuseppe De Nicolao.
A new kernel-based approach for linear system identification.
Automatica, Volume 46, Pages 81–93, 2010.

Steven Reece, Stephen J. Roberts.
An Introduction to Gaussian Processes for the Kalman Filter Expert.
In Proceedings of the 13th International Conference on Information Fusion, 2010.

Marisa Resende, Paulo J. Santos.
Short - Term Load Forecasting Using a Gaussian Process Model - Optimal Endogenous Regressor.
Technical Report ISSN: 1645-2631, Issue 10, Instituto de Engenharia de Sistemas e Computadores de Coimbra Institute of Systems Engineering and Computers INESC, 2010.

Axel Rottmann, Wolfram Burgard.
Learning non-stationary system dynamics online using Gaussian processes.
Pattern Recognition, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010.

Yunus Saatçi, Ryan Turner, Carl Edward Rasmussen.
Gaussian Process Change Point Models.
In Proceedings of the 27th International Conference on Machine Learning, 2010.

Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Barillec.
A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems.
Journal of Signal Processing Systems, Volume 61, Pages 51–59, 2010.

Q. Q. Shen, Z. H. Sun.
Online Learning Algorithm of Gaussian Process Based on Adaptive Natural Gradient for Regression.
Advanced Materials Research: Manufacturing Engineering and Automation, Volume 1847, Pages 139–141, 2010.

Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen.
State-space inference and learning with Gaussian processes.
In Proceedings of 13th International Conference on Artificial Intelligence and Statistics, Volume 9, Pages 868–875, 2010.

Yafeng Yin, Hong Man, Jing Wang, Guang Yang.
Human motion change detection by hierarchical Gaussian process dynamical model with particle filter.
In Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010, Pages 307–314, 2010.


2011

Etienne Rudolph Ackermann, Johan Pieter de Villiers, Pierre J. Cilliers.
Nonlinear dynamic systems modeling using Gaussian processes: Predicting ionospheric total electron content over South Africa.
Journal of Geophysical Research A: Space Physics, Volume 116, 2011.

Mauricio A. Álvarez, Neil D. Lawrence.
Computationally Efficient Convolved Multiple Output Gaussian Processes.
Journal of Machine Learning Research, Volume 12, Pages 1459–1500, 2011.

Kristjan Ažman, Juš Kocijan.
Dynamical systems identification using Gaussian process models with incorporated local models.
Engineering Applications of Artificial Intelligence, Volume 24, Pages 398–408, 2011.

William Becker, Keith Worden, Manuela Battipede, Cecilia Surace.
Uncertainty Analysis of a Dynamic Model of a Novel Remotely Piloted Airship.
Journal of Aircraft, Volume 48, Pages 1028–1035, 2011.

Sotirios P. Chatzis, Yiannis Demiris.
Echo state Gaussian process.
IEEE Transactions on Neural Networks, Volume 22, Pages 1435–1445, 2011.

Tianshi Chen, Henrik Ohlsson, Lennart Ljung.
On the Estimation of Transfer Functions, Regularizations and Gaussian Processes - Revisited.
In 18th IFAC World Congress, 2011.

Marc Peter Deisenroth, Henrik Ohlsson.
A general perspective on Gaussian filtering and smoothing: Explaining current and deriving new algorithms.
In American Control Conference (ACC), 2011, Pages 1807–1812, 2011.

Marc P Deisenroth, Carl Edward Rasmussen.
PILCO: A model-based and data-efficient approach to policy search.
In International Conference on Machine Learning (ICML), Pages 465–472, 2011.

Marc P Deisenroth, Carl Edward Rasmussen, Dieter Fox.
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning.
In Robotics: Science & Systems (RSS), 2011.

Guolliang Fan, Xin Zhang, Meng Ding.
Gaussian process for human motion modeling: A comparative study.
IEEE International Workshop on Machine Learning for Signal Processing, 2011.

Nuwan Gamage, Ye Chow Kuang, Rini Akmeliawati, Serge Demidenko.
Gaussian Process Dynamical Models for hand gesture interpretation in Sign Language.
Pattern Recognition Letters, Volume 32, Pages 2009–2014, 2011.

Zhiqiang Ge, Tao Chen, Zhihuan Song.
Quality prediction for polypropylene production process based on CLGPR model.
Control Engineering Practice, Volume 19, Pages 423–432, 2011.

Alexandra Grancharova, Juš Kocijan.
Explicit stochastic model predictive control of gas-liquid separator based on Gaussian process model.
In Proceedings of the International Conference Automatics and Informatics 2011, 2011.

Perry Groot, Peter Lucas, Paul van den Bosch.
Multiple-step time series forecasting with sparse Gaussian processes.
In Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC 2011), Pages 105–112, 2011.

Tomohiro Hachino, Visakan Kadirkamanathan.
Multiple Gaussian process models for direct time series forecasting.
IEEJ Transactions on Electrical and Electronic Engineering, Volume 6, Pages 245–252, 2011.

Joseph Hall, Carl Edward Rasmussen, Jan Maciejowski.
Reinforcement Learning with Reference Tracking Control in Continuous State Spaces.
In Proceedings of the 50th International Conference on Decision and Control, 2011.

Jouni Hartikainen, Jaakko Riihimäki, Simo Särkkä.
Sparse Spatio-temporal Gaussian Processes with General Likelihoods.
Artificial Neural Networks and Machine Learning - ICANN 2011, Lecture Notes in Computer Science, 2011.

Jouni Hartikainen, Simo Särkkä.
Sequential Inference for Latent Force Models.
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011, 2011.

Philipp Hennig.
Optimal reinforcement learning for Gaussian systems.
In Advances in Neural Information Processing Systems, Pages 325–333, 2011.

Andres Felipe Hernandez, Martha Grover.
Comparison of Sampling Strategies for Gaussian Process Models, with Application to Nanoparticle Dynamics.
Industrial and Engineering Chemistry Research, Volume 50, Pages 1379–1388, 2011.

Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence.
tigre: Transcription factor inference through Gaussian process reconstruction of expression for bioconductor.
Bioinformatics, Volume 27, Pages 1026–1027, 2011.

Wenjing Huang, Ke Wang, F. Jay Breidt, Richard A. Davis.
A class of stochastic volatility models for environmental applications.
Journal of Time Series Analysis, Volume 32, Pages 364–377, 2011.

Hunor Jakab, Lehel Csató.
Improving Gaussian Process Value Function Approximation in Policy Gradient Algorithms.
Artificial Neural Networks and Machine Learning - ICANN 2011, Lecture Notes in Computer Science, 2011.

Đani Juričić, Pavel Ettler, Juš Kocijan.
Fault detection based on Gaussian process models: An application to the rolling mill.
ICINCO 2011 - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics, 2011.

A. A. Kalaitzis, N. D. Lawrence.
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression.
BMC Bioinformatics, Volume 12, 2011.

Jonathan Ko, Dieter Fox.
Learning GP-BayesFilters via Gaussian process latent variable models.
Autonomous Robots, Volume 30, Pages 3–23, 2011.

Juš Kocijan.
Control Algorithms Based on Gaussian Process Models: A State-of-the-Art Survey.
In Proceedings of the Special International Conference on Complex Systems: Synergy of Control, Communications and Computing - COSY 2011, 2011.

Juš Kocijan, Dejan Petelin.
Output-Error Model Training for Gaussian Process Models.
Adaptive and Natural Computing Algorithms, Lecture Notes in Computer Science, 2011.

Michał Lewandowski, Dimitros Makris, Jean-Christoph Nebel.
Probabilistic Feature Extraction from Multivariate Time Series using Spatio-Temporal Constraints.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 6635 LNAI, Pages 173–184, 2011.

Andrew McHutchon, Carl Edward Rasmussen.
Gaussian Process Training with Input Noise.
Advances in Neural Information Processing Systems, 2011.

O. Menzer, A. Moffat, G. Lasslop, M. Reichstein.
Gaussian Process Regression for Uncertainty Estimation on Ecosystem Data.
American Geophysical Union, Fall Meeting, 2011.

Duy Nguyen-Tuong, Jan Peters.
Incremental online sparsification for model learning in real-time robot control.
Neurocomputing, Volume 74, Pages 1859–1867, 2011.

Wangdong Ni, Soon Keat Tan, Wun Jern Ng.
Recursive GPR for nonlinear dynamic process modeling.
Chemical Engineering Journal, Volume 173, Pages 636–643, 2011.

PnichHang Ou, Hengshan Wang.
Modeling and forecasting stock market volatility by Gaussian processes based on GARCH, EGARCH and GJR models.
Proceedings of the World Congress on Engineering 2011, WCE 2011, 2011.

Jóan Petur Petersen, Daniel J Jacobsen, Ole Winther.
Statistical modelling for ship propulsion efficiency.
Journal of Marine Science and Technology, Pages 1–10, 2011.

Dejan Petelin, Juš Kocijan.
Control system with evolving Gaussian process models.
IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems, 2011.

Dejan Petelin, Juš Kocijan, Alexandra Grancharova.
On-line Gaussian process model for the prediction of the ozone concentration in the air.
Comptes Rendus de L'Academie Bulgare des Sciences, Volume 64, Pages 117–124, 2011.

D Petelin, J Sindelar, J Prikryl, J Kocijan.
Financial modeling using Gaussian process models.
In Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2011 IEEE 6th International Conference on, Volume 2, Pages 672–677, 2011.

Gianluigi Pillonetto, Alessandro Chiuso, Giuseppe De Nicolao.
Prediction error identification of linear systems: A nonparametric Gaussian regression approach.
Automatica, Volume 47, Pages 291–305, 2011.

Gianluigi Pillonetto, Minh Ha Quang, Alessandro Chiuso.
A New Kernel-Based Approach for NonlinearSystem Identification.
IEEE Transactions on Automatic Control, Volume 56, Pages 2825–2840, 2011.

Shi Qu, Ronghuan Yu, Yingmei Wei, Lingda Wu.
Gaussian Process Latent Variable Models for Inverse Kinematics.
Journal of Multimedia, Volume 6, Pages 48–55, 2011.

Aditi Roy, Shamik Sural, Jayanta Mukherjee, Gerhard Rigoll.
Occlusion detection and gait silhouette reconstruction from degraded scenes.
Signal, Image and Video Processing, Volume 5, Pages 415–430, 2011.

Javier Serradilla, Jian Qing Shi, Julian A. Morris.
Fault detection based on Gaussian process latent variable models.
Chemometrics and Intelligent Laboratory Systems, Volume 109, Pages 9–21, 2011.

Jian Qing Shi, Taeryon Choi.
Gaussian Process Regression Analysis for Functional Data.
Taylor and Francis, 2011.

Xiaolin Wei, Jianyuan Min, Jinxiang Chai.
Physically-Valid Statistical Models for Human Motion Generation.
ACM Transactions on Graphics, Volume 30, 2011.

Z. Xia, J. Tang.
Characterization of Structural Dynamics With Uncertainty by Using Gaussian Processes.
ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2011.

Hongkai Xiong, Zhe Yuan, Yuan F. Zheng.
A learning-based video compression on low-quality data by unscented kalman filters with Gaussian process regression.
Proceedings - IEEE International Symposium on Circuits and Systems, 2011.

Jianfeng Xu, Koichi Takagi, Shigeyuki Sakazawa.
Human motion tracking with monocular video by introducing a graph structure into Gaussian process dynamical models.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 7087 LNCS, Pages 370–383, 2011.

Wenjin Yan, Shuangquan Hu, Yanhui Yang, Furong Gao, Tao Chen.
Bayesian migration of Gaussian process regression for rapid process modeling and optimization.
Chemical Engineering Journal, Volume 166, Pages 1095–1103, 2011.

Fu Yongfeng.
A dynamic soft-sensor modeling method based on FC-GP for 4-CBA content.
Conference Record - IEEE Instrumentation and Measurement Technology Conference, 2011.

Xu Zhao, Yun Fu, Yuncai Liu.
Human motion tracking by temporal-spatial local Gaussian process experts.
IEEE Transactions on Image Processing, Volume 20, Pages 1141–1151, 2011.

Ze Zhang, Tuopeng Tong, Kai Song.
A novel GPLS-GP algorithm and its application to air temperature prediction.
Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011, 2011.


2012

Christoph Anger, R Schrader, U Klingauf.
Unscented Kalman filter with Gaussian process degradation model for bearing fault prognosis.
In Proceedings of the European conference of the prognostics and health management society, Pages 202–213, 2012.

M. M. Atia, A. Noureldin, M. Korenberg.
Enhanced Kalman Filter for RISS/GPS Integrated Navigation using Gaussian Process Regression.
In Proceedings of the 2012 International Technical Meeting of The Institute of Navigation, Pages 1148–1156, 2012.

Er-Wei Bai.
Local Prediction Error Adjusted Gaussian Process for Nonlinear Non-Parametric System Identification.
In 16th IFAC Symposium on System Identification, Pages 101–106, 2012.

Tianshi Chen, Henrik Ohlsson, Lennart Ljung.
On the estimation of transfer functions, regularizations and Gaussian processes-Revisited.
Automatica, Volume 48, Pages 1525–1535, 2012.

Alessandro Chiuso, Gianluigi Pillonetto.
A Bayesian approach to sparse dynamic network identification.
Automatica, Volume 48, Pages 1553–1565, 2012.

Girish Chowdhary, Jonathan How, Hassan Kingravi.
Model reference adaptive control using nonparametric adaptive elements.
In Conference on Guidance Navigation and Control, Minneapolis, MN, 2012.

L. Clifton, D. A. Clifton, M. A. F. Pimentel, P. J. Watkinson, L. Tarassenko.
Gaussian Process Regression in Vital-Sign Early Warning Systems.
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012.

John P Cunningham, Zoubin Ghahramani, Carl E Rasmussen.
Gaussian Processes for time-marked time-series data.
Journal of Machine Learning Research - Proceedings Track, Volume 22, Pages 225–263, 2012.

A.C. Damianou, C.H. Ek, M.K. Titsias, N.D. Lawrence.
Manifold relevance determination.
In Proceedings of the 29th International Conference on Machine Learning (ICML), Volume 1, Pages 145–152, 2012.

M. De Paula, E. Martinez.
Probabilistic optimal control of blood glucose under uncertainty.
Computer Aided Chemical Engineering, Volume 30, Pages 1357–1361, 2012.

Marc Peter Deisenroth, Roberto Calandra, André Seyfarth, Jan Peters.
Toward Fast Policy Search for Learning Legged Locomotion.
In IROS'12, Pages 1787–1792, 2012.

Marc Peter Deisenroth, Shakir Mohamed.
Expectation Propagation in Gaussian Process Dynamical Systems.
Advances in Neural Information Processing Systems, Volume 25, Pages 2618–2626, 2012.

MP Deisenroth, J Peters.
Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise.
In European Workshop on Reinforcement Learning (EWRL 2012), Pages 1–14, 2012.

Marc Peter Deisenroth, Ryan Darby Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen.
Robust Filtering and Smoothing with Gaussian Processes.
IEEE Transactions on Automatic Control, Volume 57, Pages 1865–1871, 2012.

Dingwen Dong.
Mine Gas Emission Prediction based on Gaussian Process Model.
Procedia Engineering, Volume 45, Pages 334–338, 2012.

D.-W. Dong, S.-G. Li, X.-T. Chang, H.-F. Lin.
Prediction model of gas concentration around working face using multivariate time series.
Journal of Mining and Safety Engineering, Volume 29, Pages 135–139, 2012.

R. Grbić, D. Slišković, P. Kadlec.
Adaptive soft sensor for online prediction based on moving window Gaussian process regression.
In 2012 11th International Conference on Machine Learning and Applications, Pages 428–433, 2012.

Gregor Gregorčič, Gordon Lightbody.
Gaussian process internal model control.
International Journal of Systems Science, Volume 43, Pages 2079–2094, 2012.

Tobias Gutjahr, Holger Ulmer, Christoph Ament.
Sparse Gaussian Processes with Uncertain Inputs for Multi-Step Ahead Prediction.
In 16th IFAC Symposium on System Identification, Pages 107–112, 2012.

Tomohiro Hachino, Shoichi Yamakawa.
Non-parametric identification of continuous-time Hammerstein systems using Gaussian process model and particle swarm optimization.
Artificial Life and Robotics, Volume 17, Pages 35–40, 2012.

J. Hall, C. E. Rasmussen, J. Maciejowski.
Modelling and Control of Nonlinear Systems using Gaussian Processes with Partial Model Information.
Conference on Decision and Control (CDC), 2012.

Jouni Hartikainen, Mari Seppänen, Simo Särkkä.
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction.
In Proceedings of the 29th International Conference on Machine Learning (ICML-12), Pages 903–910, 2012.

Y. Heo, V. M. Zavala.
Gaussian process modeling for measurement and verification of building energy savings.
Energy and buildings, Volume 53, Pages 2012, 2012.

Živko Južnič-Zonta, Juš Kocijan, Xavier Flotats, Darko Vrečko.
Multi-criteria analyses of wastewater treatment bio-processes under an uncertainty and a multiplicity of steady states.
Water research, Volume 46, Pages 6121–31, 2012.

Hyuk Kang, F. C. Park.
Humanoid Motion Optimization via Nonlinear Dimension Reduction.
2012 IEEE International Conference on Robotics and Automation, Pages 1444–1449, 2012.

Juš Kocijan.
Dynamic GP models: an overview and recent developments.
In ASM'12 Proceedings of the 6th international conference on Applied Mathematics, Simulation, Modelling, Pages 38–43, 2012.

Christophe Lecomte, J J Forster, B R Mace, N S Ferguson.
Bayesian Damage Localisation at Higher Frequencies with Gaussian Process Error.
In Conference Proceedings of the Society for Experimental Mechanics Series, Pages 39–48, 2012.

Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan.
A semiparametric Bayesian approach to Wiener system identification.
In 16th IFAC Symposium on System Identification, Pages 1137–1142, 2012.

M. Niranjan W. Liu.
Gaussian process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile.
Bioinformatics, Volume 28, Pages 366–372, 2012.

Zaobao Liu, Weiya Xu, Jianfu Shao.
Gauss Process Based Approach for Application on Landslide Displacement Analysis and Prediction.
CMES - Computer Modeling in Engineering and Sciences, Volume 84, Pages 99–122, 2012.

J. M. Lourenco, P. J. Santos.
Short-term load forecasting using a Gaussian process model: The influence of a derivativeterm in the input regressor.
Intelligent Decision Technologies, Volume 6, 2012.

Max D. Morris.
Gauss Process Based Approach for Application on Landslide Displacement Analysis and Prediction.
Technometrics, Volume 54, Pages 42–50, 2012.

Duy Nguyen-Tuong, J. Peters.
Online Kernel-Based Learning for Task-Space Tracking Robot Control.
Neural Networks and Learning Systems, IEEE Transactions on, Volume 23, Pages 1417–1425, 2012.

Wangdong Ni, Soon Keat Tan, Wun Jern Ng, Steven D. Brown.
Moving-Window GPR for Nonlinear Dynamic System Modeling with Dual Updating and Dual Preprocessing.
Industrial & Engineering Chemistry Research, Volume 51, Pages 6416–6428, 2012.

Wangdong Ni, Ke Wang, Tao Chen, Wun Jern Ng, Soon Keat Tan.
GPR model with signal preprocessing and bias update for dynamic processes modeling.
Control Engineering Practice, Volume 20, Pages 1281–1292, 2012.

Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas.
Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults.
In 26th AAAI Conference on Artificial Intelligence (AAAI-12), 2012.

Carl Edward Rasmussen.
Machine Learning, Probabilistic Inference, System Identification and Control.
In 16th IFAC Symposium on System Identification, Pages 1275–1275, 2012.

Simo Särkkä, Jouni Hartikainen.
Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression.
Journal of Machine Learning Research - Proceedings Track, Volume 22, Pages 993–1001, 2012.

Harold Soh, Yiannis Demiris.
Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process.
In The 2012 International Joint Conference on Neural Networks (IJCNN), Pages 1–8, 2012.

B. Sun, H. Yao, T. Liu.
Short-term wind speed forecasting based on Gaussian process regression model.
In Proceedings of the Chinese Society of Electrical Engineering, 2012.

Chi Hay Tong, P. Furgale, T.D. Barfoot.
Gaussian Process Gauss-Newton: Non-Parametric State Estimation.
In Computer and Robot Vision (CRV), 2012 Ninth Conference on, Pages 206–213, 2012.

Ryan Turner, Carl Edward Rasmussen.
Model based learning of sigma points in unscented Kalman filtering.
Neurocomputing, Volume 80, Pages 47–53, 2012.

Yali Wang, Brahim Chaib-draa.
A Marginalized Particle Gaussian Process Regression.
In Neural Information Processing Systems 25, 2012.

Yali Wang, Brahim Chaib-draa.
An Adaptive Nonparametric Particle Filter for State Estimation.
In 2012 IEEE International Conference on Robotics and Automation, Pages 4355–4360, 2012.

Zhikun Wang, Marc Deisenroth, Heni Ben Amor, David Vogt, Bernhard Scholkopf, Jan Peters.
Probabilistic Modeling of Human Movements for Intention Inference.
In Proceedings of Robotics: Science and Systems, 2012.

Jie Yu.
Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach.
Chemical Engineering Science, Volume 82, Pages 22–30, 2012.

Zulkarnain Zainudin, Sarath Kodagoda, Linh Van Nguyen.
Mutual information based data selection in Gaussian processes for people tracking.
In Proceedings of Australasian Conference on Robotics and Automation, Pages 225–230, 2012.


2013

A Abusnina, D. Kudenko.
Adaptive Soft Sensor based on moving Gaussian process window.
In Industrial Technology (ICIT), 2013 IEEE International Conference on, Pages 1051–1056, 2013.

T. Alpcan, I Shames, M. Cantoni, G. Nair.
Learning and Information for Dual Control.
In Control Conference (ASCC), 2013 9th Asian, Pages 1–6, 2013.

M.A Alvarez, D. Luengo, N.D. Lawrence.
Linear Latent Force Models Using Gaussian Processes.
Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume 35, Pages 2693–2705, 2013.

Z. Amini, H. Rabbani.
Seizure diagnosis in children based on the electroencephalogram modellind by Gaussian process model.
Journal of Isfahan Medical School, Volume 31, Pages 985–996, 2013.

Erik Berger, David Vogt, Nooshin Haji-Ghassemi, Bernhard Jung, Heni Ben Amor.
Inferring Guidance Information in Cooperative Human-Robot Tasks.
In Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2013.

B. Bischoff, D. Nguyen-Tuong, H. Markert, A. Knoll.
Learning control under uncertainty: A probabilistic Value-Iteration approach.
In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Pages 209–214, 2013.

S. Butler, J. Ringwood, F. O'Connor..
Exploiting SCADA System Data for Wind Turbine Performance Monitoring.
Conference on Control and Fault-Tolerant Systems (SysTol) October 9-11, 2013. Nice, France, 2013.

J. Calliess, M. Osborne, S. J. Roberts.
Nonlinear adaptive hybrid control by combining Gaussian process system identification with classical control laws.
In Novel Methods for Learning and Optimization of Control Policies and Trajectories for Robotics, ICRA, 2013, 2013.

Pengfei Cao, Xionglin Luo.
Modeling of soft sensor for chemical process.
CIESC Journal, 2013.

Lester Lik Teck Chan, Yi Liu, Junghui Chen.
Nonlinear System Identification with Selective Recursive Gaussian Process Models.
Industrial & Engineering Chemistry Research, Volume 52, Pages 18276–18286, 2013.

Junghui Chen, Lester Lik Teck Chan, Yi-Cheng Cheng.
Gaussian process regression based optimal design of combustion systems using flame images.
Applied Energy, Volume 111, Pages 153–160, 2013.

N. Chen, Z. Qian, X. Meng.
Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes.
Mathematical Problems in Engineering, Volume 2013, 2013.

N. Chen, Z. Qian, X. Meng, I. T. Nabney.
Short-Term Wind Power Forecasting Using Gaussian Processes.
International joint conference on Artificial Intelligence IJCAI'13, 2013.

K. Chen, Y. Zhang, J. Yi.
Modeling of Rider-Bicycle Interactions with Learned Dynamics on Constrained Embedding Manifolds.
2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013, 2013.

G. Chowdhary, H. Kingravi, J.P. How, P. Vela.
Bayesian nonparameteric model reference adaptive control using Gaussian processes.
52nd IEEE Conference on Decision and Control, 2013.

G. Chowdhary, H.A. Kingravi, J.P. How, P.A. Vela.
Bayesian nonparametric adaptive control of time-varying systems using Gaussian processes.
Proceedings of the American Control Conference, 2013.

Girish Chowdhary, Miao Liu, Robert C. Grande, Thomas J. Walsh, Jonathan P. How.
Off-Policy Reinforcement Learning with Gaussian Processes.
Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2013.

O.M. Cliff, T. Sildomar, Monteiro.
Evaluating Techniques for Learning a Feedback Controller for Low-Cost Manipulators.
IEEE International Conference on Intelligent Robots and Systems, 2013.

M. Deisenroth, G. Neumann, J. Peters.
A Survey on Policy Search for Robotics.
Foundations and Trends in Robotics, Volume 2, Issue 1-2, Pages 1–142, 2013.

A. H. ELSheikh, C. C. Pain, F. Fang, J. L. M. A. Gomes, I. M. Navon.
Parameter estimation of subsurface flow models using iterative regularized ensemble Kalman filter.
Stochastic Environmental Research and Risk Assessment, Volume 27, Pages 877–897, 2013.

Peter Englert, Alexandros Paraschos, Marc Deisenroth, Jan Peters.
Probabilistic Model-based Imitation Learning.
Adaptive Behavior, Volume 21, Pages 388–403, 2013.

Peter Englert, Alexandros Paraschos, Jan Peters, Marc Peter Deisenroth.
Model-based Imitation Learning by Probabilistic Trajectory Matching.
In ICRA, Pages 1922–1927, 2013.

Roger Frigola, Fredrik Lindsten, Thomas B Schon, Carl Rasmussen.
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.
Advances in Neural Information Processing Systems 26, 2013.

Roger Frigola, Carl E Rasmussen.
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes.
In Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on, 2013.

R.C. Grande, G. Chowdhary, J.P. How.
Nonparametric adaptive control using Gaussian Processes with online hyperparameter estimation.
In Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on, Pages 861–867, 2013.

Ratko Grbić, Dino Kurtagić, Dražen Slišković.
Stream water temperature prediction based on Gaussian process regression.
Expert Systems with Applications, Volume Volume 40, Pages 7407–7414, 2013.

Ratko Grbić, Dražen Slišković, Petr Kadlec.
Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models.
Computers & Chemical Engineering, Volume 58, Pages 84–97, 2013.

Tomohiro Hachino, Hitoshi Takata, Shigeru Nakayama, Seiji Fukushima, Yasutaka Igarashi.
Application of Firefly Algorithm to Gaussian Process-based Prediction of Electric Power Damage Caused by Typhoons.
International Journal of Computer Science and Electronics Engineering (IJCSEE), Volume 1, Issue 3, Pages 440–444, 2013.

Tomohiro Hachino, Tatsuya Ueda, Hitoshi Takata.
Gaussian Process Regression for Prediction of Electric Power Damage Caused by Typhoons Considering Nonstationarity of Damage.
Journal of Signal Processing, Volume 17, Issue 3, Pages 61–68, 2013.

M. Han, W. Ren, M. Xu.
Prediction of multivariate time series with sparse Gaussian process echo state network.
Proceedings of the 2013 International Conference on Intelligent Control and Information Processing, ICICIP 2013, 2013.

Prasad Hemakumara, Salah Sukkarieh.
Learning UAV Stability and Control Derivatives Using Gaussian Processes.
IEEE Transactions on Robotics, Pages 1–12, 2013.

Sheng Hong, Zheng Zhou, Chuan Lv, Hongyi Guo.
Prognosis for insulated gate bipolar transistor based on Gaussian Process Regression.
In Prognostics and Health Management (PHM), 2013 IEEE Conference on, Pages 1–5, 2013.

E.D. Klenske, M.N. Zeilinger, B. Scholkopf, P. Hennig.
Nonparametric Dynamics Estimation for Time Periodic Systems.
In Communication, Control, and Computing (Allerton), 2013 51st Annual Allerton Conference on, Pages 486–493, 2013.

Juš Kocijan.
Incorporating knowledge about model structure in the identification of Gaussian-process models.
In Recent Advances in Telecommunications, Signals and Systems, Pages 124–129, 2013.

Peng Kou, Feng Gao, Xiaohong Guan.
Sparse online warped Gaussian process for wind power probabilistic forecasting.
Applied Energy, Volume 108, Pages 410–428, 2013.

Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Gerhard Neumann.
Data-Efficient Generalization of Robot Skills with Contextual Policy Search.
In AAAI, 2013.

Duehee Lee, Joonhyun Kim, R. Baldick.
Stochastic Optimal Control of the Storage System to Limit Ramp Rates of Wind Power Output.
Smart Grid, IEEE Transactions on, Volume 4, Pages 2256–2265, 2013.

Yu Lei, Huizhong Yang.
Combination model soft sensor based on Gaussian process and Bayesian committee machine.
CIESC Journal, Volume 64, Issue 12, Pages 4434–4438, 2013.

Xiangyu Li, Xianwen Gao, Yongbin Cui, Kun Li.
Dynamic liquid level modeling of sucker-rod pumping systems based on Gaussian process regression.
In Natural Computation (ICNC), 2013 Ninth International Conference on, Pages 917–922, 2013.

Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan.
Bayesian semiparametric Wiener system identification.
Automatica, Volume 49, Pages 2053–2063, 2013.

Zitao Liu, Lei Wu, Milos Hauskrecht.
Modeling clinical time series using Gaussian process sequences.
In SIAM international conference on data mining, Pages 623–631, 2013.

J.M. Lourenço, J.M. Lemos, J.S. Marques.
Control of neuromuscular blockade with Gaussian process models.
Biomedical Signal Processing and Control, Volume 8, Pages 244–254, 2013.

J.M. Maciejowski, X. Yang.
Fault tolerant control using Gaussian processes and model predictive control.
Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France., 2013.

Hae Young Noh, Ram Rajagopal.
Data-Driven Forecasting Algorithms for Building Energy Consumption.
In Proceedings SPIE Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, Volume 8692, 2013.

Sooho Park, S.K. Mustafa, K. Shimada.
Learning-based robot control with localized sparse online Gaussian process.
In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, Pages 1202–1207, 2013.

Sooho Park, S.K. Mustafa, K. Shimada.
Learning based robot control with sequential Gaussian process.
In Robotic Intelligence In Informationally Structured Space (RiiSS), 2013 IEEE Workshop on, Pages 120–127, 2013.

Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel Lázaro-Gredilla, Ignacio Santamaría.
Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances.
IEEE Signal Processing Magazine, Volume 30, Pages 40–50, 2013.

L. Peternel, J. Babič.
Learning of compliant human-robot interaction using full-body haptic interface.
Advanced Robotics, Volume 27, Pages 1003–1012, 2013.

Dejan Petelin, Alexandra Grancharova, Juš Kocijan.
Evolving Gaussian process models for prediction of ozone concentration in the air.
Simulation Modelling Practice and Theory, Volume 33, Pages 68–80, 2013.

Gianluigi Pillonetto.
Consistent identification of Wiener systems: A machine learning viewpoint.
Automatica, Volume 49, Pages 2704–2712, 2013.

G. Pillonetto, T. Chen, L. Ljung.
Kernel-based model order selection for linear system identification.
IFAC Proceedings Volumes (IFAC-PapersOnline), Volume 11, Pages 257–262, 2013.

Jan Přikryl.
Graphics card as a cheap supercomputer.
In Programs and Algorithms of Numerical Matematics, Volume 16, Pages 162–167, Institute of Mathematics, Academy of Sciences of the Czech Republic, Prague, 2013.

S. Särkkä, J. Hartikainen.
Non-linear noise adaptive Kalman filtering via variational Bayes.
In Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on, Pages 1–6, 2013.

Simo Särkkä, Arno Solin, Jouni Hartikainen.
Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering.
IEEE Signal Processing Magazine, Volume 30, Pages 51–61, 2013.

S. Sedai, M. Bennamoun, D. Q. Huynh..
A Gaussian Process Guided Particle Filter for Tracking 3D Human Pose in Video.
IEEE Transactions on Image Processing, Volume 22, Pages 4286 – 4300, 2013.

Gang Shen, Yu Cao.
A Gaussian Process Based Model Predictive Controller for Nonlinear Systems with Uncertain Input-Output Delay.
Applied Mechanics and Materials, Volume 433–435, Pages 1015–1020, 2013.

Arno Solin, Simo Särkkä.
Infinite-dimensional Bayesian filtering for detection of quasiperiodic phenomena in spatiotemporal data.
Phys. Rev. E, Volume 88, Issue 5, Pages 052909, 2013.

Yanyu Su, Yan Wu, Harold Soh, Zhijiang Du, Yiannis Demiris.
Enhanced Kinematic Model for Dexterous Manipulation with an Underactuated Hand.
In IROS, Pages 2493–2499, 2013.

Taiji Suzuki, Kazuyuki Aihara.
Nonlinear system identification for prostate cancer and optimality of intermittent androgen suppression therapy.
Mathematical biosciences, 2013.

N. Taubert, M. Löffler, N. Ludolph, A. Christensen, D. Endres, M.A. Giese.
A virtual reality setup for controllable, stylized real-time interactions between humans and avatars with sparse Gaussian process dynamical models.
In Proceedings - SAP 2013: ACM Symposium on Applied Perception, Pages 41–44, 2013.

V. Vitelli, E. Zio.
Approximate Gaussian Process Regression with Sparse Functional Learning of Inducing Points for Components Condition Monitoring.
Chemical Engineering Transactions, Volume 33, Pages 907–912, 2013.

Y. Wang, B. Chaib-Draa.
A KNN based kalman filter Gaussian process regression.
International joint conference on Artificial Intelligence IJCAI'13, 2013.

Zhikun Wang, Katharina Mülling, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters.
Probabilistic Movement Modeling for Intention Inference in Human-Robot Interaction.
The International Journal of Robotics Research, Volume 32, Pages 841–858, 2013.

Z. Xia, J. Tang.
Characterization of Dynamic Response of Structures With Uncertainty by Using Gaussian Processes.
Journal of Vibration and Acoustics-Transactions of the ASME, Volume 135, Issue 5, 2013.

Wei Xi-qing, Song Shen-min.
Model-free cubature Kalman filter and its application.
Control and Decision, Volume 28, 2013.

Juan Yan, Kang Li, Er-Wei Bai.
Prediction error adjusted Gaussian Process for short-term wind power forecasting.
In Intelligent Energy Systems (IWIES), 2013 IEEE International Workshop on, Pages 173–178, 2013.

J. Yu, K. Chen, J. Mori, M. M. Rashid.
A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction.
Energy, Volume 61, Pages 673–686, 2013.

J. Yu, K. Chen, J. Mori, M.M. Rashid.
Multi-kernel Gaussian process regression and Bayesian model averaging based nonlinear state estimation and quality prediction of multiphase batch processes.
Proceedings of the American Control Conference, 2013.

J. Yu, K. Chen, M. M. Rashid.
A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty.
Chemical Engineering Science, Volume 93, Pages 96–109, 2013.


2014

Ali Abusnina, Daniel Kudenko, Rolf Roth.
Selection of Covariance Functions in Gaussian Process-based Soft Sensors.
In Industrial Technology (ICIT), 2014 IEEE International Conference on, 2014.

Ali Abusnina, Daniel Kudenko, Rolf Roth.
Gaussian Process-Based Inferential Control System.
International Joint Conference SOCO'14-CISIS'14-ICEUTE'14, Advances in Intelligent Systems and Computing, 2014.

Adriana Amicarelli, Olga Quintero, Fernando di Sciascio.
Behavior comparison for biomass observers in batch processes.
Asia-Pacific Journal of Chemical Engineering, Volume 9, Pages 81–92, 2014.

Tim Barfoot, Chi Hay Tong andbuk Simo Särkkä.
Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression.
In Proceedings of Robotics: Science and Systems, 2014.

Felix Berkenkamp, Angela P. Schoellig.
Learning-based robust control: guaranteeing stability while improving performance.
In Proc. of the Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014.

Hildo Bijl, Jan-Willem van Wingerden, Michel Verhaegen.
Applying Gaussian Processes to Reinforcement Learning for Fixed-Structure Controller Synthesis.
In World Congress, Volume 19, Pages 10391–10396, 2014.

Ilias Bilionis, Emil M Constantinescu, Mihai Anitescu.
Data-driven model for solar irradiation based on satellite observations.
Solar Energy, Volume 110, Pages 22–38, 2014.

B. Bischoff, D. Nguyen-Tuong, H. van Hoof, A. McHutchon, C.E. Rasmussen, A. Knoll, J. Peters, M.P. Deisenroth.
Policy Search For Learning Robot Control Using Sparse Data.
In Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014.

B. Bocsi, L. Csato, J. Peters.
Indirect Robot Model Learning for Tracking Control.
Advanced Robotics, Volume 28, Pages 1–11, 2014.

B. Bocsi, H. Jakab, L. Csato.
Simulation-Extrapolation Gaussian Processes for Input Noise Modeling.
In Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2014 16th International Symposium on, Pages 189–195, 2014.

Carl Boettiger, Marc Mangel, Stephan Munch.
Avoiding tipping points in fisheries management through Gaussian process dynamic programming.
Proceedings of the Royal Society of London B: Biological Sciences, Volume 282, Pages n/a, 2014.

J. Boedecker, J. T. Springenberg, J. Wülfing, M. Riedmiller.
Approximate real-time optimal control based on sparse Gaussian process models.
In 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), Pages 1–8, 2014.

Luca Bortolussi, Guido Sanguinetti.
A Statistical Approach for Computing Reachability of Non-linear and Stochastic Dynamical Systems.
Quantitative Evaluation of Systems, Lecture Notes in Computer Science, 2014.

Giulio Bottegal, Aleksandr Y Aravkin, Håkan Hjalmarsson, Gianluigi Pillonetto.
Outlier robust system identification: a Bayesian kernel-based approach.
IFAC Proceedings Volumes, Volume 47, Pages 1073–1078, 2014.

W. Bukhari, S.-M. Hong.
Real-time prediction of respiratory motion using a cascade structure of an extended Kalman filter and support vector regression.
Physics in Medicine and Biology, 2014.

M.C. Burkhart, Y. Heo, V.M. Zavala.
Measurement and verification of building systems under uncertain data: A Gaussian process modeling approach.
Energy and Buildings, Volume 75, Pages 189–198, 2014.

Gang Cao, E.M.-K. Lai, F. Alam.
Particle swarm optimization for convolved Gaussian process models.
In Neural Networks (IJCNN), 2014 International Joint Conference on, Pages 1573–1578, 2014.

Francesca P. Carli.
On the maximum entropy property of the first-order stable spline kernel and its implications.
In Control Applications (CCA), 2014 IEEE Conference on, Pages 409–414, 2014.

Hongmei Chen, Xianghong Cheng, Haipeng Wang, Xu Han.
Dealing with Observation Outages within Navigation Data using Gaussian Process Regression.
Journal of Navigation, Volume 67, Issue 04, Pages 603–615, 2014.

A. Chiuso, T. Chen, L. Ljung, G. Pillonetto.
On the design of multiple kernels for nonparametric linear system identification.
In 53rd IEEE Conference on Decision and Control, Pages 3346–3351, 2014.

Johan Dahlin, Fredrik Lindsten.
Particle filter-based Gaussian process optimisation for parameter inference.
In Proceedings of the 19th IFAC World Congress, Pages 8675–8680, 2014.

M. P. Deisenroth, P. Englert, J. Peters, D. Fox.
Multi-Task Policy Search for Robotics.
In IEEE International Conference on Robotics and Automation (ICRA), 2014.

Bing Dong, KheePoh Lam.
A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting.
Building Simulation, Volume 7, Pages 89–106, 2014.

C. Earls, G. Hooker.
Bayesian covariance estimation and inference in latent Gaussian process models.
Journal of Statistical Methodology, Volume 18, Pages 79–100, 2014.

Roger Frigola, Yutian Chen, Carl Rasmussen.
Variational Gaussian Process State-Space Models.
Advances in Neural Information Processing Systems 27, 2014.

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen.
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM.
In Proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC), Pages 4097–4102, 2014.

Adam Gonczarek, Jakub M. Tomczak.
Manifold Regularized Particle Filter for Articulated Human Motion Tracking.
Advances in Systems Science, Advances in Intelligent Systems and Computing, 2014.

Robert C. Grande, Girish Chowdhary, Jonathan P. How.
Experimental Validation of Bayesian Nonparametric Adaptive Control Using Gaussian Processes.
J. Aerospace Inf. Sys., Volume 11, Pages 565–578, 2014.

Robert Grande, Thomas Walsh, Jonathan How.
Sample Efficient Reinforcement Learning with Gaussian Processes.
In Proceedings of the 31st International Conference on Machine Learning (ICML-14), Pages 1332–1340, 2014.

T. Hachino, Y. Hashiguchi, H. Takata, S. Fukushima, Y. Igarashi.
Local Gaussian process models for identification of discrete-time Hammerstein Systems.
ICIC Express Letters, Volume 8, Pages 173–179, 2014.

T. Hachino, K. Matsushita, H. Takata, S. Fukushima, Y. Igarashi.
Identification of continuous-time nonlinear systems via local Gaussian process models.
IEEJ Transactions on Electronics, Information and Systems, Volume 134, Pages 1708–1715, 2014.

Tomohiro Hachino, Hitoshi Takata, Shigeru Nakayama, Ichiro Iimura, Seiji Fukushima, Yasutaka Igarashi.
Gaussian Process Model Identification Using Artificial Bee Colony Algorithm and Its Application to Modeling of Power Systems.
International Journal of Electrical, Computer, Electronics and Communication Engineering, Volume 8, Pages 437 – 442, 2014.

Nooshin Haji-Ghassemi, Marc Deisenroth.
Analytic Long-Term Forecasting with Periodic Gaussian Processes.
Journal of Machine Learning Research, Volume 33, Pages 303–311, 2014.

Ming Hu, Zonghai Sun.
Multimodel Nonlinear Predictive Control with Gaussian Process Model.
Unifying Electrical Engineering and Electronics Engineering, Lecture Notes in Electrical Engineering, 2014.

He-ming Jia, Wen-long Song, Hong-wei Mu, Yan-ting Che.
Research on Initial Alignment Technology Based on GP-SRCDKF.
Computer Engineering, Volume 40, Pages 195–198, 2014.

Sanket Kamthe, Jan Peters, Marc P. Deisenroth.
Multi-Modal Filtering for Non-linear Estimation.
In International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), 2014.

Juš Kocijan, Alexandra Grancharova.
Application of Gaussian Processes to the Modelling and Control in Process Engineering.
Innovations in Intelligent Machines-5, Studies in Computational Intelligence, 2014.

L. Král, J. Pruher, M. Šimandl.
Gaussian process based dual adaptive control of nonlinear stochastic systems.
In Control and Automation (MED), 2014 22nd Mediterranean Conference of, Pages 1074–1079, 2014.

Ivan Madjarov, Juš Kocijan, Alexandra Grancharova, Bogdan Shishedjiev.
Towards a Service-Based Framework for Environmental Data Processing.
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5, Issue 4, 2014.

J. Mahler, S. Krishnan, M. Laskey, S. Sen, A. Murali, B. Kehoe, S. Patil, Jiannan Wang, M. Franklin, P. Abbeel, K. Goldberg.
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression.
In Automation Science and Engineering (CASE), 2014 IEEE International Conference on, Pages 532–539, 2014.

L. Muñoz-González, M. Lázaro-Gredilla, Aníbal R. Figueiras-Vidal.
Laplace approximation with Gaussian Processes for volatility forecasting.
In Cognitive Information Processing (CIP), 2014 4th International Workshop on, Pages 1–6, 2014.

Gerhard Neumann, Christian Daniel, Alexandros Paraschos, Andras Kupcsik, Jan Peters.
Learning Modular Policies for Robotics.
Frontiers in Computational Neuroscience, Volume 8, Pages 1–13, 2014.

P. Ngo, J. Das, J. Ogle, J. Thomas, W. Anderson, R.N. Smith.
Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data.
In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, Pages 2250–2256, 2014.

Wangdong Ni, Lars Nørgaard, Morten Mørup.
Non-linear calibration models for near infrared spectroscopy.
Analytica Chimica Acta, Volume 813, Pages 1–14, 2014.

C.J. Ostafew, A.P. Schoellig, T.D. Barfoot.
Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments.
In Robotics and Automation (ICRA), 2014 IEEE International Conference on, Pages 4029–4036, 2014.

Dejan Petelin, Jus Kocijan.
Evolving Gaussian process models for predicting chaotic time-series.
In Evolving and Adaptive Intelligent Systems (EAIS), 2014 IEEE Conference on, Pages 1–8, 2014.

Gianluigi Pillonetto, Francesco Dinuzzo, Tianshi Chen, Giuseppe De Nicolao, Lennart Ljung.
Kernel methods in system identification, machine learning and function estimation: A survey.
Automatica, Volume 50, Pages 657 – 682, 2014.

Christian Preusche, Christoph Anger, Uwe Klingauf.
Evaluation of the Training Process of three different Prognostic Approaches based on the Gaussian Process.
In Proceedings of the European conference of the prognostics and health management society, Pages 202–213, 2014.

Jakub Prüher, Miroslav Šimandl.
Gaussian process based recursive system identification.
Journal of Physics: Conference Series, Volume 570, Pages 012002, 2014.

Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen Roberts, Nicholas R. Jennings.
Efficient State-space Inference of Periodic Latent Force Models.
Journal of Machine Learning Research, Volume 15, Pages 2337–2397, 2014.

M. Rupp, M. R. Bauer, R. Wilcken, A. Lange, M. Reutlinger, F. M. Boeckler, G. Schneider.
Machine Learning Estimates of Natural Product Conformational Energies.
PLOS Computational Biology, Volume 10, 2014.

Behrooz Safarinejadian, Elham Kowsari.
Fault detection in non-linear systems based on GP-EKF and GP-UKF algorithms.
Systems Science & Control Engineering, Volume 2, Pages 610–620, 2014.

Ahmed Shokry, Antonio Espuña.
Sequential Dynamic Optimization of Complex Nonlinear Processes based on Kriging Surrogate Models.
Procedia Technology, Volume 15, Pages 376 - 387, 2014, 2nd International Conference on System-Integrated Intelligence: Challenges for Product and Production Engineering.

Arno Solin, Simo Särkkä.
Explicit link between periodic covariance functions and state space models.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, Volume 33, Pages 904–912, 2014.

Martin Stepančič, Juš Kocijan.
Vodenje nestabilnega hidravličnega sistema z modelom na podlagi Gaussovih procesov.
Industrijski forum IRT, 2014, (in Slovene).

Martin Stepančič, Juš Kocijan.
Prediktivno vodenje nestabilnega sistema s sprotno identifikacijo verjetnostnega modela.
Ventil, Volume 20, Issue 5, Pages 374–380, 2014, (in Slovene).

Zonghai Sun.
Gaussian Process adaptive control of nonlinear system base on online algorithm.
In Control Conference (CCC), 2014 33rd Chinese, Pages 8791-8794, 2014.

A. Y. Sun, D. Wang, X. Xu.
Monthly streamflow forecasting using Gaussian Process Regression.
Journal of Hydrology, Volume 511, Pages 72–81, 2014.

J.T. Thorson, K. Ono, S.B. Munch.
A Bayesian approach to identifying and compensating for model misspecification in population models.
Ecology, Volume 95, Pages 329–341, 2014.

Nils Tietze, Urich Konigorski, Duy Nguyen-Tuong.
Local Gaussian process regression for model-based calibration of engine control units.
5th Simulation and Testing for Automotive Electronics, 2014.

Kenji Urai, Yuya Okadome, Yoshihiro Nakata, Yutaka Nakamura, Hiroshi Ishiguro.
Estimation of physical interaction between a musculoskeletal robot and its surroundings.
Artificial Life and Robotics, Volume 19, Pages 193–200, 2014.

Dmytro Velychko, Dominik Endres, Nick Taubert, MartinA. Giese.
Coupling Gaussian Process Dynamical Models with Product-of-Experts Kernels.
Artificial Neural Networks and Machine Learning – ICANN 2014, Lecture Notes in Computer Science, 2014.

Yali Wang, Marcus A Brubaker, Brahim Chaib-draa, Raquel Urtasun.
Bayesian Filtering with Online Gaussian Process Latent Variable Models.
Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Canada, 2014.

Ziyou Wang, J. Kinugawa, Hongbo Wang, K. Kazahiro.
A human motion estimation method based on GP-UKF.
In Information and Automation (ICIA), 2014 IEEE International Conference on, Pages 1228–1232, 2014.

Keith Worden, Graeme Manson, ElizabethJ. Cross.
On Gaussian Process NARX Models and Their Higher-Order Frequency Response Functions.
Solving Computationally Expensive Engineering Problems, Springer Proceedings in Mathematics & Statistics, 2014.

Yulai Zhang, Guiming Luo, Fuan Pu.
Power Load Forecasting based on Multi-task Gaussian Process.
In Proceedings of the IFAC 19th World Congress, Pages 3651–3656, 2014.

Jing Zhao, Shiliang Sun.
Variational Dependent Multi-output Gaussian Process Dynamical Systems.
Discovery Science, Lecture Notes in Computer Science, 2014.

K. Zhou, G. Liang, J. Tang.
Efficient model updating using Bayesian probabilistic framework based on measured vibratory response.
In Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014, Volume 9063, 2014.

Justina Žuraskiené, Paul Kirk, Thomas Thorne, John Pinney, Michael Stumpf.
Derivative processes for modelling metabolic fluxes.
Systems biology, Volume 30, Pages 1892–1898, 2014.


2015

Ki-Uhn Ahn, Deuk-Woo Kim, Young-Jin Kim, Cheol-Soo Park, In-Han Kim.
Gaussian Process Model for Control of an Existing Building.
Energy Procedia, Volume 78, Pages 2136 – 2141, 2015.

L.O. Avila, Ernesto C. Martinez.
A Grid-Based Tool for Optimal Performance Monitoring of an Artificial Pancreas.
VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014, IFMBE Proceedings, 2015.

A.M. Axelrod, H.A. Kingravi, G.V. Chowdhary.
Gaussian process based subsumption of a parasitic control component.
In American Control Conference (ACC), 2015, Pages 2888–2893, 2015.

F. Berkenkamp, A.P. Schoellig.
Safe and robust learning control with Gaussian processes.
In Control Conference (ECC), 2015 European, Pages 2496-2501, 2015.

Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schoen, Michel Verhaegen.
Online sparse Gaussian process regression using FITC and PITC approximations.
IFAC-PapersOnLine, Volume 48, Issue 28, Pages 703–708, 2015.

Silvia Bonettini, Alessandro Chiuso, Marco Prato.
A scaled gradient projection method for Bayesian learning in dynamical systems.
SIAM Journal on Scientific Computing, Volume 37, Pages A1297–A1318, 2015.

Giulio Bottegal, Gianluigi Pillonetto, Håkan Hjalmarsson.
Bayesian kernel-based system identification with quantized output data.
IFAC-PapersOnLine, Volume 48, Pages 455–460, 2015.

Giulio Bottegal, Riccardo S. Risuleo, Håkan Hjalmarsson.
Blind system identification using kernel-based methods.
IFAC-PapersOnLine, Volume 48, Pages 466–471, 2015.

S.P. Chatzis, D. Kosmopoulos.
A Latent Manifold Markovian Dynamics Gaussian Process.
Neural Networks and Learning Systems, IEEE Transactions on, Volume 26, Pages 70–83, 2015.

Tianshi Chen, Gianluigi Pillonetto, Alessandro Chiuso, Lennart Ljung.
Spectral analysis of the DC kernel for regularized system identification.
In 2015 54th IEEE Conference on Decision and Control (CDC), Pages 4017–4022, 2015.

Xin Chen, Penghuan Xie, Yong He, Min Wu.
Coordinated learning based on time-sharing tracking framework and Gaussian regression for continuous multi-agent systems.
Engineering Applications of Artificial Intelligence, Volume 41, Pages 56–64, 2015.

G. Chowdhary, H.A. Kingravi, J.P. How, P.A. Vela.
Bayesian Nonparametric Adaptive Control Using Gaussian Processes.
Neural Networks and Learning Systems, IEEE Transactions on, Volume 26, Pages 537–550, 2015.

Andreas Damianou, Neil D. Lawrence.
Semi-described and semi-supervised learning with Gaussian processes.
31st Conference on Uncertainty in Artificial Intelligence, 2015.

Mohamed Darwish, Pepijn Cox, Gianluigi Pillonetto, Roland Toth.
Bayesian identification of LPV Box-Jenkins models.
In 54th IEEE Conference on Decision and Control (CDC), Pages 66–71, 2015.

Mohamed Darwish, Gianluigi Pillonetto, Roland Toth.
Perspectives of Orthonormal Basis Functions Based Kernels in Bayesian System Identification.
In 2015 54th IEEE Conference on Decision and Control (CDC), Pages 2713–2718, 2015.

M.P. Deisenroth, D. Fox, C.E. Rasmussen.
Gaussian Processes for Data-Efficient Learning in Robotics and Control.
Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume 37, Pages 408–423, 2015.

Pradipto Ghosh, Bruce A. Conway.
Spatial statistical point prediction guidance for heating-rate-limited aeroassisted orbital transfer.
Acta Astronautica, Volume 111, Pages 257 – 269, 2015.

P. Guo, X. Wang.
Vibration monitoring by Gaussian process regression for wind turbine towers.
Dongli Gongcheng Xuebao/Journal of Chinese Society of Power Engineering, Volume 35, Pages 380–386, 2015.

Tomohiro Hachino, Hitoshi Takata, Seiji Fukushima, Yasutaka Igarashi.
Model Predictive Control of Electric Power Systems Based on Gaussian Process Predictors.
Journal of Automation and Control Engineering, Volume 3, Pages 418–424, 2015.

Hyuk Kang, F. C. Park.
Motion optimization using Gaussian process dynamical models.
Multibody System Dynamics, Volume 34, Pages 307–325, 2015.

K. Kronander, M. Khansari, A. Billard.
Incremental motion learning with locally modulated dynamical systems.
Robotics and Autonomous Systems, Volume 70, Pages 52 – 62, 2015.

Zitao Liu, Milos Hauskrecht.
Clinical time series prediction: Toward a hierarchical dynamical system framework.
Artificial Intelligence in Medicine, Volume 65, Pages 5 - 18, 2015, Artificial Intelligence in Medicine AIME 2013.

César Lincoln C. Mattos, José Daniel A. Santos, Guilherme A. Barreto.
An Empirical Evaluation of Robust Gaussian Process Models for System Identification.
Pages 172–180, 2015.

B. Michini, T.J. Walsh, A.-A. Agha-Mohammadi, J.P. How.
Bayesian Nonparametric Reward Learning From Demonstration.
Robotics, IEEE Transactions on, Volume 31, Pages 369–386, 2015.

J. Ngeo, T. Tamei, K. Ikeda, T. Shibata.
Modeling dynamic high-DOF finger postures from surface EMG using nonlinear synergies in latent space representation.
In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, Pages 2095-2098, 2015.

Yuya Okadome, Yutaka Nakamura, Hiroshi Ishiguro.
Sampling-based Motion Planning with a Prediction Model using Fast Gaussian Process Regression.
IEEJ Transactions on Electronics, Information and Systems, Volume 135, Pages 526–533, 2015, (in Japanese).

Chris J. Ostafew, Angela P. Schoellig, Timothy D. Barfoot.
Conservative to confident: treating uncertainty robustly within learning-based control.
In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Pages 421–427, 2015.

Y. Pan, E. A. Theodorou.
Data-driven differential dynamic programming using Gaussian processes.
In 2015 American Control Conference (ACC), Pages 4467-4472, 2015.

Gianluigi Pillonetto.
Identification of hybrid systems using stable spline kernels.
In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), Pages 1–6, 2015.

Gianluigi Pillonetto, Alessandro Chiuso.
Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator.
Automatica, Volume 58, Pages 106 – 117, 2015.

I. Proimadis, H. J. Bijl, J. W. van Wingerden.
A kernel based approach for LPV subspace identification.
IFAC-PapersOnLine, Volume 48, Pages 97–102, 2015.

Jakub Prüher, Ladislav Král.
Functional Dual Adaptive Control with Recursive Gaussian Process Model.
In Journal of Physics: Conference Series, Volume 659, 2015.

R. Quintero, I. Parra, D.F. Llorca, M.A. Sotelo.
Pedestrian Intention and Pose Prediction through Dynamical Models and Behaviour Classification.
In Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, Pages 83-88, 2015.

Riccardo S. Risuleo, Giulio Bottegal, Håkan Hjalmarsson.
A kernel-based approach to Hammerstein system identication.
IFAC-PapersOnLine, Volume 48, Pages 1011–1016, 2015.

R. S. Risuleo, G. Bottegal, H. Hjalmarsson.
On the estimation of initial conditions in kernel-based system identification.
In 2015 54th IEEE Conference on Decision and Control (CDC), Pages 1120–1125, 2015.

R. S. Risuleo, G. Bottegal, H. Hjalmarsson.
A new kernel-based approach to overparameterized Hammerstein system identification.
In 2015 54th IEEE Conference on Decision and Control (CDC), Pages 115–120, 2015.

D. Romeres, G. Pillonetto, A. Chiuso.
Identification of stable models via nonparametric prediction error methods.
In 2015 European Control Conference (ECC), Pages 2044–2049, 2015.

J. Schreiter, P. Englert, D. Nguyen-Tuong, M. Toussaint.
Sparse Gaussian Process Regression for Compliant, Real-Time Robot Control.
In Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), 2015.

Ahmed Shokry, Francesca Audino, Patricia Vicente, Gerard Escudero, Montserrat Perez Moya, Moises Graells, Antonio Espuña.
Modeling and Simulation of Complex Nonlinear Dynamic Processes Using Data Based Models: Application to Photo-Fenton Process.
12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 2015.

H. Soh, Y. Demiris.
Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes.
Neural Networks and Learning Systems, IEEE Transactions on, Volume 26, Pages 522–536, 2015.

Martin Stepančič, Alexandra Grancharova, Juš Kocijan.
Adaptive MPC based on probabilistic black-box input-output model.
Comptes Rendus de l Academie Bulgare des Sciences, Volume 68, Pages 767–774, 2015.

Pei Sun, Junghui Chen, Lei Xie.
Self-active and recursively selective Gaussian process models for nonlinear distributed parameter systems.
Chemical Engineering Science, Volume 123, Pages 125 – 136, 2015.

S. Urban, M. Ludersdorfer, P. van der Smagt.
Sensor Calibration and Hysteresis Compensation With Heteroscedastic Gaussian Processes.
IEEE Sensors Journal, Volume 15, Pages 6498–6506, 2015.

Michail D. Vrettas, Manfred Opper, Dan Cornford.
Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations.
Phys. Rev. E, Volume 91, Issue 1, Pages 012148, 2015.

Xiaoke Yang, J.M. Maciejowski.
Fault tolerant control using Gaussian processes and model predictive control.
International Journal of Applied Mathematics and Computer Science, Volume 25, Issue 1, Pages 133–148, 2015.

Fu Yongfeng, Xu Ouguan, Chen Weijie, Ji Haifeng.
Adaptive soft sensor modeling method based on multi-model dynamic fusion and its industrial application.
In Instrumentation and Measurement Technology Conference (I2MTC), 2015 IEEE International, Pages 1308-1313, 2015.

M. Zorzi, A. Chiuso.
A Bayesian approach to sparse plus low rank network identification.
In 2015 54th IEEE Conference on Decision and Control (CDC), Pages 7386-7391, 2015.


2016

T. Beckers, S. Hirche.
Stability of Gaussian Process State Space Models.
In Proceedings of the European Control Conference (ECC), 2016.

T. Beckers, S. Hirche.
Equilibrium distributions and stability analysis of Gaussian Process State Space Models.
In 2016 IEEE 55th Conference on Decision and Control (CDC), Pages 6355–6361, 2016.

F. Berkenkamp, R. Moriconi, A. P. Schoellig, A. Krause.
Safe learning of regions of attraction for uncertain, nonlinear systems with Gaussian processes.
In 2016 IEEE 55th Conference on Decision and Control (CDC), Pages 4661–4666, 2016.

Felix Berkenkamp, Angela P Schoellig, Andreas Krause.
Safe Controller Optimization for Quadrotors with Gaussian Processes.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2016.

Giulio Bottegal, Aleksandr Y. Aravkin, Håkan Hjalmarsson, Gianluigi Pillonetto.
Robust EM kernel-based methods for linear system identification.
Automatica, Volume 67, Pages 114–126, 2016.

G. Cao, E. M-K Lai, F. Alam.
Gaussian Process based Model Predictive Control for Linear Time Varying systems.
In 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC), Pages 251–256, 2016.

A. Carron, M. Todescato, R. Carli, L. Schenato, G. Pillonetto.
Machine learning meets Kalman Filtering.
In 2016 IEEE 55th Conference on Decision and Control (CDC), Pages 4594–4599, 2016.

Tanmoy Chatterjee, Souvik Chakraborty, Rajib Chowdhury.
A bi-level approximation tool for the computation of FRFs in stochastic dynamic systems.
Mechanical Systems and Signal Processing, Volume 70-71, Pages 484 - 505, 2016.

Lester Lik Teck Chan, Tao Chen, Junghui Chen.
PID based nonlinear processes control model uncertainty improvement by using Gaussian process model.
Journal of Process Control, Volume 42, Pages 77–89, 2016.

Tianshi Chen, Tohid Ardeshiri, Francesca P Carli, Alessandro Chiuso, Lennart Ljung, Gianluigi Pillonetto.
Maximum entropy properties of discrete-time first-order stable spline kernel.
Automatica, Volume 66, Pages 34–38, 2016.

T. Chen, G. Pillonetto, A. Chiuso, L. Ljung.
Continuous-time DC kernel — A stable generalized first order spline kernel.
In 2016 IEEE 55th Conference on Decision and Control (CDC), Pages 4647–4652, 2016.

A Chiuso.
Regularization and Bayesian learning in dynamical systems: Past, present and future.
Annual Reviews in Control, Volume 41, Pages 24–38, 2016.

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence.
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes.
Journal of Machine Learning Research, Volume 17, Pages 1–62, 2016.

PL Green.
Towards the Diagnosis and Simulation of Discrepancies in Dynamical Models.
Model Validation and Uncertainty Quantification, Volume 3, 2016.

Toni Karvonen, Simo Sarkkä.
Approximate state-space Gaussian processes via spectral transformation.
In Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on, Pages 1–6, 2016.

Edgar D. Klenske, Philipp Hennig.
Dual Control for Approximate Bayesian Reinforcement Learning.
Journal of Machine Learning Research, Volume 17, Pages 1–30, 2016.

E.D. Klenske, M.N. Zeilinger, B. Scholkopf, P. Hennig.
Gaussian Process-Based Predictive Control for Periodic Error Correction.
Control Systems Technology, IEEE Transactions on, Volume 24, Pages 110-121, 2016.

Juš Kocijan.
Modelling and Control of Dynamic Systems Using Gaussian Process Models.
Springer International Publishing, 2016.

Juš Kocijan, Dejan Petelin.
Closed-Loop Control with Evolving Gaussian Process Models.
Complex Systems, 2016.

John Lataire, Tianshi Chen.
Transfer function and transient estimation by Gaussian process regression in the frequency domain.
Automatica, Volume 72, Pages 217–229, 2016.

César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A Barreto, Neil D Lawrence.
Recurrent Gaussian Processes.
In International Conference on Learning Representations (ICLR), 2016.

César Lincoln C. Mattos, Andreas Damianou, Guilherme A Barreto, Neil Lawrence.
Latent Autoregressive Gaussian Process Models for Robust System Identification.
In 11th IFAC Symposium on Dynamics and Control of Process System (DYCOPS), 2016.

J. R. Medina, S. Endo, S. Hirche.
Impedance-based Gaussian Processes for predicting human behavior during physical interaction.
In 2016 IEEE International Conference on Robotics and Automation (ICRA), Pages 3055–3061, 2016.

Shayegan Omidshafiei, Ali-Akbar Agha-Mohammadi, Yu Fan Chen, Nazim Kemal Ure, Shih-Yuan Liu, Brett T Lopez, Rajeev Surati, Jonathan P How, John Vian.
Measurable Augmented Reality for Prototyping Cyberphysical Systems: A Robotics Platform to Aid the Hardware Prototyping and Performance Testing of Algorithms.
IEEE Control Systems, Volume 36, Pages 65–87, 2016.

Gianluigi Pillonetto.
A new kernel-based approach to hybrid system identification.
Automatica, Volume 70, Pages 21–31, 2016.

Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung.
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint.
Automatica, Volume 69, Pages 137–149, 2016.

Rishik Ranjan, Biao Huang, Alireza Fatehi.
Robust Gaussian process modeling using EM algorithm.
Journal of Process Control, Volume 42, Pages 125–136, 2016.

R. S. Risuleo, G. Bottegal, H. Hjalmarsson.
Kernel-based system identification from noisy and incomplete input-output data.
In 2016 IEEE 55th Conference on Decision and Control (CDC), Pages 2061–2066, 2016.

Jens Schreiter, Duy Nguyen-Tuong, Marc Toussaint.
Efficient sparsification for Gaussian process regression.
Neurocomputing, Volume 192, Pages 29–37, 2016.

Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B Schön.
Computationally efficient Bayesian learning of Gaussian process state space models.
In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Pages 213–221, 2016.

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Henner Schmidt, Anne Romer, Jan Peters.
Stability of Controllers for Gaussian Process Forward Models.
In Proceedings of The 33rd International Conference on Machine Learning, Pages 545–554, 2016.

Weili Xiong, Wei Zhang, Baoguo Xu, Biao Huang.
JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy.
Computers & Chemical Engineering, Volume 90, Pages 260–267, 2016.

J. Zhao, S. Sun.
High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction.
IEEE Transactions on Intelligent Transportation Systems, Volume 17, Pages 2014–2019, 2016.

Jing Zhao, Shiliang Sun.
Variational Dependent Multi-output Gaussian Process Dynamical Systems.
Journal of Machine Learning Research, Volume 17, Pages 1–36, 2016.

E-mail:jus[dot]kocijan[at]ijs[dot]si