| Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels F Tobar, T Bui, R Turner Neural Information Processing Systems 28, 2015 | 102 | 2015 |
| Spectral Mixture Kernels for Multi-Output Gaussian Processes G Parra, F Tobar Advances in Neural Information Processing Systems, 2017 | 101 | 2017 |
| Multikernel Least Mean Square Algorithm FA Tobar, SY Kung, DP Mandic IEEE Transactions on Neural Networks and Learning Systems 25 (2), 265 - 277, 2014 | 101 | 2014 |
| Robust Detection of Extreme Events Using Twitter: Worldwide Earthquake Monitoring B Poblete, J Guzmán, J Maldonado, F Tobar IEEE Transactions on Multimedia 20 (10), 2551-2561, 2018 | 76 | 2018 |
| Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical performance comparison M Orchard, F Tobar, G Vachtsevanos Studies in Informatics and Control 18 (4), 295-304, 2009 | 75 | 2009 |
| Quaternion Reproducing Kernel Hilbert Spaces: Existence and Uniqueness Conditions FA Tobar, DP Mandic IEEE Transactions on Information Theory, 2014 | 66 | 2014 |
| Compositionally-warped Gaussian processes G Rios, F Tobar Neural Networks 118, 235-246, 2019 | 56 | 2019 |
| Predicting nationwide obesity from food sales using machine learning J Dunstan, M Aguirre, M Bastías, C Nau, TA Glass, F Tobar Health Informatics Journal 26 (1), 652-663, 2020 | 55 | 2020 |
| MOGPTK: The multi-output Gaussian process toolkit T de Wolff, A Cuevas, F Tobar Neurocomputing 424, 49-53, 2021 | 54 | 2021 |
| A novel augmented complex valued kernel LMS FA Tobar, A Kuh, DP Mandic 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM …, 2012 | 46 | 2012 |
| Bayesian nonparametric spectral estimation F Tobar Advances in Neural Information Processing Systems 31, 10127-10137, 2018 | 43 | 2018 |
| The Wasserstein-Fourier distance for stationary time series E Cazelles, A Robert, F Tobar IEEE Transactions on Signal Processing, 2020 | 41 | 2020 |
| Bayesian learning with Wasserstein barycenters J Backhoff, J Fontbona, G Rios, F Tobar ESAIM: Probability and Statistics 26, 436–472, 2022 | 30 | 2022 |
| Unsupervised State-Space Modelling Using Reproducing Kernels F Tobar, PM Djuric, DP Mandic IEEE Transactions on Signal Processing, In print, 2015 | 30 | 2015 |
| Anomaly detection in power generation plants using similarity-based modeling and multivariate analysis FA Tobar, L Yacher, R Paredes, ME Orchard Proceedings of the 2011 American Control Conference, 1940-1945, 2011 | 27 | 2011 |
| The quaternion kernel least squares. FA Tobar, DP Mandic ICASSP, 6128-6132, 2013 | 24 | 2013 |
| Learning non-Gaussian Time Series using the Box-Cox Gaussian Process G Rios, F Tobar 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 21 | 2018 |
| Echo state network and variational autoencoder for efficient one-class learning on dynamical systems D Cabrera, F Sancho, M Cerrada, RV Sánchez, F Tobar Journal of Intelligent & Fuzzy Systems, 1-11, 2018 | 21 | 2018 |
| Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters F Tobar, I Castro, J Silva, M Orchard Pattern Recognition Letters 105, 200-206, 2018 | 20 | 2018 |
| Band-limited Gaussian processes: The sinc kernel F Tobar Advances in Neural Information Processing Systems 32, 12749-12759, 2019 | 19 | 2019 |
Danilo MandicProf. of Machine Intelligence, Dept of Electrical and Electronic Eng., Imperial College London, UKVerified email at imperial.ac.uk
