Anomaly detection in power generation plants using similarity-based modeling and multivariate analysis. - Felipe A. Tobar; Luis Yacher; Rodrigo Paredes; Marcos E. Orchard (ACC, 2011)

A novel augmented complex valued kernel LMS. - Felipe A. Tobar; Anthony Kuh; Danilo P. Mandic (SAM, 2012)

The quaternion kernel least squares. - Felipe A. Tobar; Danilo P. Mandic (ICASSP, 2013)

Estimation of financial indices volatility using a model with time-varying parameters. - Felipe A. Tobar; Marcos E. Orchard; Danilo P. Mandic; Anthony G. Constantinides (CIFEr, 2014)

A particle filtering based kernel HMM predictor. - Felipe A. Tobar; Danilo P. Mandic (ICASSP, 2014)

Quaternion Reproducing Kernel Hilbert Spaces: Existence and Uniqueness Conditions. - Felipe A. Tobar; Danilo P. Mandic (IEEE Trans. Inf. Theory, 2014)

Multikernel Least Mean Square Algorithm. - Felipe A. Tobar; Sun-Yuan Kung; Danilo P. Mandic (IEEE Trans. Neural Networks Learn. Syst., 2014)

The widely linear quaternion recursive total least squares. - Thiannithi Thanthawaritthisai; Felipe A. Tobar; Anthony G. Constantinides; Danilo P. Mandic (ICASSP, 2015)

Modelling of complex signals using gaussian processes. - Felipe A. Tobar; Richard E. Turner (ICASSP, 2015)

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. - Felipe A. Tobar; Thang D. Bui; Richard E. Turner (NIPS, 2015)

Design of Positive-Definite Quaternion Kernels. - Felipe A. Tobar; Danilo P. Mandic (IEEE Signal Process. Lett., 2015)

Unsupervised State-Space Modeling Using Reproducing Kernels. - Felipe A. Tobar; Petar M. Djuric; Danilo P. Mandic (IEEE Trans. Signal Process., 2015)

Modelling time series via automatic learning of basis functions. - Felipe A. Tobar; Richard E. Turner (SAM, 2016)

Initialising kernel adaptive filters via probabilistic inference. - Iván Castro; Cristóbal Silva; Felipe A. Tobar (DSP, 2017)

Improving sparsity in kernel adaptive filters using a unit-norm dictionary. - Felipe A. Tobar (DSP, 2017)

Spectral Mixture Kernels for Multi-Output Gaussian Processes. - Gabriel Parra; Felipe A. Tobar (NIPS, 2017)

Recovering Latent Signals From a Mixture of Measurements Using a Gaussian Process Prior. - Felipe A. Tobar; Gonzalo Rios; Tomas Valdivia; Pablo Guerrero (IEEE Signal Process. Lett., 2017)

Learning non-Gaussian Time Series using the Box-Cox Gaussian Process. - Gonzalo Rios; Felipe A. Tobar (IJCNN, 2018)

Bayesian Nonparametric Spectral Estimation. - Felipe A. Tobar (NeurIPS, 2018)

Bayesian Learning with Wasserstein Barycenters. - Gonzalo Rios; Julio D. Backhoff Veraguas; Joaquín Fontbona; Felipe A. Tobar (CoRR, 2018)

Echo state network and variational autoencoder for efficient one-class learning on dynamical systems. - Diego Cabrera 001; Fernando Sancho; Mariela Cerrada; René-Vinicio Sánchez; Felipe A. Tobar (J. Intell. Fuzzy Syst., 2018)

Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters. - Felipe A. Tobar; Iván Castro; Jorge F. Silva; Marcos E. Orchard (Pattern Recognit. Lett., 2018)

Robust Detection of Extreme Events Using Twitter: Worldwide Earthquake Monitoring. - Barbara Poblete; Jheser Guzman; Jazmine A. Maldonado Flores; Felipe A. Tobar (IEEE Trans. Multim., 2018)

Low-pass Filtering as Bayesian Inference. - Cristóbal Valenzuela; Felipe A. Tobar (ICASSP, 2019)

Band-Limited Gaussian Processes: The Sinc Kernel. - Felipe A. Tobar (NeurIPS, 2019)

Compositionally-warped Gaussian processes. - Gonzalo Rios; Felipe A. Tobar (Neural Networks, 2019)

Gaussian Process Imputation of Multiple Financial Series. - Taco de Wolff; Alejandro Cuevas; Felipe A. Tobar (ICASSP, 2020)

Predicting nationwide obesity from food sales using machine learning. - Jocelyn Dunstan; Marcela Aguirre; Magdalena Bastías; Claudia Nau; Thomas A. Glass; Felipe A. Tobar (Health Informatics J., 2020)

Bayesian autoregressive spectral estimation. - Alejandro Cuevas; Sebastián López; Danilo P. Mandic; Felipe A. Tobar (LA-CCI, 2021)

Detection of blue whale vocalisations using a temporal-domain convolutional neural network. - Bryan Sagredo; Sonia Español-Jiménez; Felipe A. Tobar (LA-CCI, 2021)

A novel notion of barycenter for probability distributions based on optimal weak mass transport. - Elsa Cazelles; Felipe A. Tobar; Joaquín Fontbona (NeurIPS, 2021)

Streaming computation of optimal weak transport barycenters. - Elsa Cazelles; Felipe A. Tobar; Joaquín Fontbona (CoRR, 2021)

Late reverberation suppression using U-nets. - Diego León; Felipe A. Tobar (CoRR, 2021)

Studying the Interplay between Information Loss and Operation Loss in Representations for Classification. - Jorge F. Silva; Felipe A. Tobar; Mario Vicuña; Felipe Cordova (CoRR, 2021)

MOGPTK: The multi-output Gaussian process toolkit. - Taco de Wolff; Alejandro Cuevas; Felipe A. Tobar (Neurocomputing, 2021)

Data Science for Engineers: A Teaching Ecosystem. - Felipe A. Tobar; Felipe Bravo-Marquez; Jocelyn Dunstan; Joaquín Fontbona; Alejandro Maass; Daniel Remenik; Jorge F. Silva (IEEE Signal Process. Mag., 2021)

The Wasserstein-Fourier Distance for Stationary Time Series. - Elsa Cazelles; Arnaud Robert; Felipe A. Tobar (IEEE Trans. Signal Process., 2021)

Bayesian Reconstruction of Fourier Pairs. - Felipe A. Tobar; Lerko Araya-Hernández; Pablo Huijse; Petar M. Djuric (IEEE Trans. Signal Process., 2021)

Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures. - Matías Altamirano; Felipe A. Tobar (AISTATS, 2022)

On the Interplay between Information Loss and Operation Loss in Representations for Classification. - Jorge Silva; Felipe A. Tobar (AISTATS, 2022)

Modeling Neonatal EEG Using Multi-Output Gaussian Processes. - Víctor Caro; Jou-Hui Ho; Scarlet Witting; Felipe A. Tobar (IEEE Access, 2022)

On machine learning and the replacement of human labour: anti-Cartesianism versus Babbage's path. - Felipe A. Tobar; Rodrigo González (AI Soc., 2022)

Greedy Online Change Point Detection. - Jou-Hui Ho; Felipe A. Tobar (MLSP, 2023)

Gaussian process deconvolution. - Felipe A. Tobar; Arnaud Robert; Jorge F. Silva (CoRR, 2023)

Asynchronous Graph Generators. - Christopher P. Ley; Felipe A. Tobar (CoRR, 2023)

Computationally-efficient initialisation of GPs: The generalised variogram method. - Felipe A. Tobar; Elsa Cazelles; Taco de Wolff (Trans. Mach. Learn. Res., 2023)

Detection of Manatee Vocalisations Using the Audio Spectrogram Transformer. - Stefano Schiappacasse; Taco de Wolff; Yann Henaut; Regina Cervera; Aviva Charles; Felipe A. Tobar (MLSP, 2024)

Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning. - Roberto Barceló; Cristóbal Alcázar; Felipe Tobar (CoRR, 2024)

Stochastic gradient descent for barycenters in Wasserstein space. - Julio Backhoff; Joaquín Fontbona; Gonzalo Rios; Felipe Tobar (J. Appl. Probab., 2025)

Towards SFW sampling for diffusion models via external conditioning. - Camilo Carvajal Reyes; Joaquín Fontbona; Felipe Tobar (CoRR, 2025)

Diffusion Self-Weighted Guidance for Offline Reinforcement Learning. - Augusto Tagle; Javier Ruiz-del-Solar; Felipe Tobar (CoRR, 2025)

Asynchronous graph generator. - Christopher P. Ley; Felipe Tobar (Signal Process., 2026)