Publications

My Google Scholar profile can be seen here.

Preprints

[P1] D. León, F. Tobar (2021), Late reverberation suppression using u-nets

Journal articles

[J17] F. Tobar, R. González (2021), On machine learning and the replacement of human labour: Anti-cartesianism versus babbage’s path, AI & Society, [Springer]

[J16] F. Tobar, F. Bravo-Marquez, J. Dunstan, J. Fontbona, A. Maass, D. Remenik, J. F. Silva (2021), Data science for engineers: A teaching ecosystem, IEEE Signal Processing Magazine, vol. 38 (3), p. 144-153, [arXiv, IEEEXplore]

[J15] E. Cazelles, A. Robert, F. Tobar (2021), The Wasserstein-Fourier distance for stationary time series, IEEE Transactions on Signal Processing, vol. 69, p. 709-721, [arXiv], [IEEEXplore]

[J14] T. de Wolff, A. Cuevas, F. Tobar (2021), MOGPTK: The multi-output Gaussian process toolkit, Elsevier Neurocomputing, vol. 424, p. 49 - 53, [arXiv], [Science Direct]

[J13] F. Tobar, L. Araya-Hernández, P. Huijse, P. Djuric (2021), Bayesian reconstruction of Fourier pairs, IEEE Transactions on Signal Processing, vol. 69, p. 73-87, [arXiv], [IEEEXplore]

[J12] J. Dunstan, M. Aguirre, M. Bastías, C. Nau, T. A. Glass, F. Tobar (2020), Predicting nationwide obesity from food sales using machine learning, Health Informatics J, vol. 26 (1), p. 652-663, [SAGE]

[J11] G. Rios, F. Tobar (2019), Compositionally-warped Gaussian processes, Neural Networks, vol. 118, p. 235 - 246, [arXiv], [Science Direct]

[J10] B. Poblete, J. Guzman, J. Maldonado, F. Tobar (2018), Robust detection of extreme events using Twitter: Worldwide earthquake monitoring, IEEE Transactions on Multimedia, vol. 20 (10), p. 2551-2561, [IEEEXplore]

[J9] D. Cabrera, F. Sancho, M. Cerrada, R.-V. Sánchez, F. Tobar (2018), Echo state network and variational autoencoder for efficient one-class learning on dynamical systems, Journal of Intelligent & Fuzzy Systems., vol. 34 (6), p. 3799-3809, [IOS Press]

[J8] F. Tobar, I. Castro, J. Silva, M. Orchard (2018), Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters, Pattern Recognition Letters, vol. 105, p. 200 - 206, [Elsevier]

[J7] F. Tobar, G. Rios, T. Valdivia, P. Guerrero (2017), Recovering latent signals from a mixture of measurements using a Gaussian process prior, IEEE Signal Processing Letters, vol. 24 (2), p. 231-235, [arXiv, IEEEXplore]

[J6] F. Tobar, D. Mandic (2015), Design of positive-definite quaternion kernels, IEEE Signal Processing Letters, vol. 22 (11), p. 2117 - 2121, [IEEEXplore]

[J5] F. Tobar, P. Djurić, D. Mandic (2015), Unsupervised state-space modelling using reproducing kernels, IEEE Trans. on Signal Processing, vol. 63 (19), p. 5210 - 5221, [IEEEXplore]

[J4] F. Tobar, D. Mandic (2014), Quaternion reproducing kernel Hilbert spaces: Existence and uniqueness conditionss, IEEE Trans. on Inf. Theory, vol. 60 (9), p. 5736-5749, [IEEEXplore]

[J3] F. Tobar, S-Y. Kung, D. Mandic (2014), Multikernel least mean square algorithm, IEEE Trans. on Neural Networks and Learning Systems, vol. 25 (2), p. 265-277, [IEEEXplore]

[J2] F. Tobar, M. Orchard (2012), Study of financial systems volatility using suboptimal estimation algorithms, Studies in Informatics and Control, vol. 21 (1), p. 59-66, [PDF]

[J1] M. Orchard, F. Tobar, G. Vachtsevanos (2009), Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical performance comparison, Studies in Informatics and Control, vol. 18 (4), p. 295-304, [PDF]

Conference articles

[C25] E. Cazelles, F. Tobar, J. Fontbona (2021), Streaming computation of optimal weak transport barycenters, Advances on neural information processing systems (to appear), [arXiv]

[C24] B. Sagredo, C. Español-Jiménez, F. Tobar (2021), Detection of blue whale vocalisations using a temporal-domain convolutional neural network, Proc. Of IEEE LACCI (to appear), [arXiv]

[C23] A. Cuevas, S. López, D. Mandic, F. Tobar (2021), Bayesian autoregressive spectral estimation, Proc. Of IEEE LACCI (to appear), [arXiv]

[C22] T. de Wolff, A. Cuevas, F. Tobar (2020), Gaussian process imputation of multiple financial series, Proc. Of IEEE ICASSP, p. 8444-8448, [arXiv], [IEEEXplore]

[C21] F. Tobar (2019), Band-limited Gaussian processes: The sinc kernel, Advances in neural information processing systems 32, p. 12749-12759, [arXiv], [NeurIPS]

[C20] C. Valenzuela, F. Tobar (2019), Low-pass filtering as Bayesian inference, Proc. Of IEEE ICASSP, p. 3367-3371, [arXiv],[IEEEXplore]

[C19] F. Tobar (2018), Bayesian nonparametric spectral estimation, Advances in neural information processing systems 31, p. 10148-10158, (spotlight) [arXiv], [NeurIPS], [GitHub]

[C18] G. Rios, F. Tobar (2018), Learning non-Gaussian time series using the Box-Cox Gaussian process, Proc. Of IJCNN, p. 1-8, [arXiv], [IEEEXplore]

[C17] G. Parra, F. Tobar (2017), Spectral mixture kernels for multi-output Gaussian processes, Advances in neural information processing systems 30, p. 6681-6690, [arXiv], [NeurIPS]

[C16] A. Cuevas, A. Veragua, S. Español, G. Chiang, F. Tobar (2017), Unsupervised blue whale call detection using multiple time-frequency features, Proc. Of IEEE Chilecon, [IEEEXplore]

[C15] L. Araya-Hernández, J. Silva, A. Osses, F. Tobar (2017), A Bayesian mixture-of-Gaussians model for astronomical observations in interferometry, Proc. Of IEEE Chilecon, [IEEEXplore]

[C14] F. Tobar (2017), Improving sparsity in kernel adaptive filters using a unit-norm dictionary, Proc. Of ieee dsp, p. 1-5, [arXiv], [IEEEXplore]

[C13] I. Castro, C. Silva, F. Tobar (2017), Initialising kernel adaptive filters via probabilistic inference, Proc. Of ieee dsp, p. 1-5, [arXiv, IEEEXplore] (best paper award)

[C12] D. Cabrera, F. Sancho, F. Tobar (2017), Combining reservoir computing and variational inference for efficient one-class learning on dynamical systems, Proc. Of the IEEE SDPC, [IEEEXplore] (best paper award)

[C11] F. Tobar, R. Turner (2016), Modelling time series via automatic learning of basis functions, Proc. Of IEEE SAM, p. 2209-2213, [IEEEXplore]

[C10] F. Tobar, T. Bui, R. Turner (2015), Learning stationary time series using Gaussian processes with nonparametric kernels, Advances in neural information processing systems 28, p. 3483-3491, (spotlight) [NeurIPS]

[C9] F. Tobar, R. Turner (2015), Modelling of complex signals using Gaussian processes, Proc. Of IEEE ICASSP, p. 2209-2213, [IEEEXplore]

[C8] T. Thanthawaritthisai, F. Tobar, A. Constantinides, D. Mandic (2015), The widely linear quaternion recursive total least squares, Proc. Of IEEE ICASSP, p. 3357-3361

[C7] F. Tobar, M. Orchard, D. Mandic, A. Constantinides (2014), Estimation of financial indices volatility using a model with time-varying parameters, Proc. Of IEEE Computational Intelligence for Financial Engineering and Economics, p. 318-324

[C6] F. Tobar, D. Mandic (2014), A particle filtering based kernel HMM predictor, Proc. Of IEEE ICASSP, p. 8019-8023

[C5] F. Tobar, D. Mandic (2013), The quaternion kernel least squares, Proc. Of IEEE ICASSP, p. 6128-6132

[C4] A. Ahrabian, D. Looney, F. Tobar, J. Hallatt, D. Mandic (2012), Noise assisted multivariate empirical mode decomposition applied to Doppler radar data, Proc. Of IEEE SSPD, p. 1-4

[C3] F. Tobar, D. Mandic (2012), Multikernel least squares estimation, Proc. Of IEEE SSPD, p. 1-5

[C2] F. Tobar, A. Kuh, D. Mandic (2012), A novel augmented complex valued kernel LMS, Proc. Of IEEE SAM, p. 473-476

[C1] F. Tobar, L. Yacher, R. Paredes, M. Orchard (2011), Anomaly detection in power generation plants using similarity-based modeling and multivariate analysis, Proc. Of the American Control Conference (ACC), p. 1940-1945

Unpublished

[U3] J. Backhoff-Veraguas, J. Fontbona, G. Rios, F. Tobar (2018), Bayesian learning with Wasserstein barycenters, [arXiv]

[U2] F. Tobar, T. Bui, R. Turner (2015), Design of covariance functions using inter-domain inducing variables, NIPS 2015 - time series workshop, [Best paper award]

[U1] F. Tobar, D. Mandic (2015), High-dimensional kernels: Tricks and treats, Trends in Digital Signal Processing: A Festschrift in Honour of A.G. Constantinides