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ORCIDFelipe A. Tobar
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2020 – today
- 2025
[j18]Julio Backhoff
, Joaquín Fontbona, Gonzalo Rios, Felipe Tobar:
Stochastic gradient descent for barycenters in Wasserstein space. J. Appl. Probab. 62(1): 15-43 (2025)- 2024
- [c24]Stefano Schiappacasse, Taco de Wolff, Yann Henaut, Regina Cervera, Aviva Charles, Felipe A. Tobar:
Detection of Manatee Vocalisations Using the Audio Spectrogram Transformer. MLSP 2024: 1-6 - [i24]Roberto Barceló, Cristóbal Alcázar, Felipe Tobar:
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning. CoRR abs/2410.08315 (2024) - 2023
- [j17]Felipe A. Tobar, Elsa Cazelles, Taco de Wolff:
Computationally-efficient initialisation of GPs: The generalised variogram method. Trans. Mach. Learn. Res. 2023 (2023) - [c23]Jou-Hui Ho, Felipe A. Tobar:
Greedy Online Change Point Detection. MLSP 2023: 1-6 - [i23]Felipe A. Tobar, Arnaud Robert, Jorge F. Silva:
Gaussian process deconvolution. CoRR abs/2305.04871 (2023) - [i22]Jou-Hui Ho, Felipe A. Tobar:
Greedy online change point detection. CoRR abs/2308.07012 (2023) - [i21]Christopher P. Ley, Felipe A. Tobar:
Asynchronous Graph Generators. CoRR abs/2309.17335 (2023) - 2022
- [j16]Víctor Caro, Jou-Hui Ho, Scarlet Witting, Felipe A. Tobar:
Modeling Neonatal EEG Using Multi-Output Gaussian Processes. IEEE Access 10: 32912-32927 (2022) - [j15]Felipe A. Tobar, Rodrigo González:
On machine learning and the replacement of human labour: anti-Cartesianism versus Babbage's path. AI Soc. 37(4): 1459-1471 (2022) - [c22]Matías Altamirano, Felipe A. Tobar:
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures. AISTATS 2022: 3204-3218 - [c21]Jorge Silva, Felipe A. Tobar:
On the Interplay between Information Loss and Operation Loss in Representations for Classification. AISTATS 2022: 4853-4871 - [i20]Matías Altamirano, Felipe A. Tobar:
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures. CoRR abs/2202.09233 (2022) - [i19]Felipe A. Tobar, Elsa Cazelles, Taco de Wolff:
Computationally-efficient initialisation of GPs: The generalised variogram method. CoRR abs/2210.05394 (2022) - 2021
- [j14]Taco de Wolff, Alejandro Cuevas, Felipe A. Tobar:
MOGPTK: The multi-output Gaussian process toolkit. Neurocomputing 424: 49-53 (2021) - [j13]Felipe A. Tobar, Felipe Bravo-Marquez, Jocelyn Dunstan, Joaquín Fontbona, Alejandro Maass, Daniel Remenik, Jorge F. Silva:
Data Science for Engineers: A Teaching Ecosystem. IEEE Signal Process. Mag. 38(3): 144-153 (2021) - [j12]Felipe A. Tobar, Lerko Araya-Hernández, Pablo Huijse, Petar M. Djuric:
Bayesian Reconstruction of Fourier Pairs. IEEE Trans. Signal Process. 69: 73-87 (2021) - [j11]Elsa Cazelles, Arnaud Robert, Felipe A. Tobar:
The Wasserstein-Fourier Distance for Stationary Time Series. IEEE Trans. Signal Process. 69: 709-721 (2021) - [c20]Alejandro Cuevas, Sebastián López, Danilo P. Mandic, Felipe A. Tobar:
Bayesian autoregressive spectral estimation. LA-CCI 2021: 1-7 - [c19]Bryan Sagredo, Sonia Español-Jiménez, Felipe A. Tobar:
Detection of blue whale vocalisations using a temporal-domain convolutional neural network. LA-CCI 2021: 1-5 - [c18]Elsa Cazelles, Felipe A. Tobar, Joaquín Fontbona:
A novel notion of barycenter for probability distributions based on optimal weak mass transport. NeurIPS 2021: 13575-13586 - [i18]Felipe A. Tobar, Felipe Bravo-Marquez, Jocelyn Dunstan, Joaquín Fontbona, Alejandro Maass, Daniel Remenik, Jorge F. Silva:
Data Science for Engineers: A Teaching Ecosystem. CoRR abs/2101.06119 (2021) - [i17]Elsa Cazelles, Felipe A. Tobar, Joaquín Fontbona:
Streaming computation of optimal weak transport barycenters. CoRR abs/2102.13380 (2021) - [i16]Diego León, Felipe A. Tobar:
Late reverberation suppression using U-nets. CoRR abs/2110.02144 (2021) - [i15]Bryan Sagredo, Sonia Español-Jiménez, Felipe A. Tobar:
Detection of blue whale vocalisations using a temporal-domain convolutional neural network. CoRR abs/2110.02151 (2021) - [i14]Jorge F. Silva, Felipe A. Tobar, Mario Vicuña, Felipe Cordova:
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification. CoRR abs/2112.15238 (2021) - 2020
- [j10]Jocelyn Dunstan, Marcela Aguirre, Magdalena Bastías, Claudia Nau, Thomas A. Glass, Felipe A. Tobar:
Predicting nationwide obesity from food sales using machine learning. Health Informatics J. 26(1) (2020) - [c17]Taco de Wolff, Alejandro Cuevas, Felipe A. Tobar:
Gaussian Process Imputation of Multiple Financial Series. ICASSP 2020: 8444-8448 - [i13]Taco de Wolff, Alejandro Cuevas, Felipe A. Tobar:
MOGPTK: The Multi-Output Gaussian Process Toolkit. CoRR abs/2002.03471 (2020) - [i12]Taco de Wolff, Alejandro Cuevas, Felipe A. Tobar:
Gaussian process imputation of multiple financial series. CoRR abs/2002.05789 (2020)
2010 – 2019
- 2019
- [j9]Gonzalo Rios, Felipe A. Tobar:
Compositionally-warped Gaussian processes. Neural Networks 118: 235-246 (2019) - [c16]Cristóbal Valenzuela, Felipe A. Tobar:
Low-pass Filtering as Bayesian Inference. ICASSP 2019: 3367-3371 - [c15]Felipe A. Tobar:
Band-Limited Gaussian Processes: The Sinc Kernel. NeurIPS 2019: 12728-12738 - [i11]Cristobal Valenzuela, Felipe A. Tobar:
Low-pass filtering as Bayesian inference. CoRR abs/1902.03427 (2019) - [i10]Felipe A. Tobar, Gonzalo Rios:
Compositionally-Warped Gaussian Processes. CoRR abs/1906.09665 (2019) - [i9]Felipe A. Tobar:
Band-Limited Gaussian Processes: The Sinc Kernel. CoRR abs/1909.07279 (2019) - [i8]Elsa Cazelles, Arnaud Robert, Felipe A. Tobar:
The Wasserstein-Fourier Distance for Stationary Time Series. CoRR abs/1912.05509 (2019) - 2018
- [j8]Diego Cabrera, Fernando Sancho, Mariela Cerrada, René-Vinicio Sánchez, Felipe A. Tobar:
Echo state network and variational autoencoder for efficient one-class learning on dynamical systems. J. Intell. Fuzzy Syst. 34(6): 3799-3809 (2018) - [j7]Felipe A. Tobar, Iván Castro, Jorge F. Silva, Marcos E. Orchard:
Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters. Pattern Recognit. Lett. 105: 200-206 (2018) - [j6]Barbara Poblete, Jheser Guzman, Jazmine A. Maldonado Flores, Felipe A. Tobar:
Robust Detection of Extreme Events Using Twitter: Worldwide Earthquake Monitoring. IEEE Trans. Multim. 20(10): 2551-2561 (2018) - [c14]Gonzalo Rios, Felipe A. Tobar:
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process. IJCNN 2018: 1-8 - [c13]Felipe A. Tobar:
Bayesian Nonparametric Spectral Estimation. NeurIPS 2018: 10148-10158 - [i7]Gonzalo Rios, Felipe A. Tobar:
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process. CoRR abs/1803.07102 (2018) - [i6]Gonzalo Rios, Julio D. Backhoff Veraguas, Joaquín Fontbona, Felipe A. Tobar:
Bayesian Learning with Wasserstein Barycenters. CoRR abs/1805.10833 (2018) - [i5]Felipe A. Tobar:
Bayesian Nonparametric Spectral Estimation. CoRR abs/1809.02196 (2018) - 2017
- [j5]Felipe A. Tobar, Gonzalo Rios, Tomas Valdivia, Pablo Guerrero:
Recovering Latent Signals From a Mixture of Measurements Using a Gaussian Process Prior. IEEE Signal Process. Lett. 24(2): 231-235 (2017) - [c12]Iván Castro, Cristóbal Silva, Felipe A. Tobar:
Initialising kernel adaptive filters via probabilistic inference. DSP 2017: 1-5 - [c11]Felipe A. Tobar:
Improving sparsity in kernel adaptive filters using a unit-norm dictionary. DSP 2017: 1-5 - [c10]Gabriel Parra, Felipe A. Tobar:
Spectral Mixture Kernels for Multi-Output Gaussian Processes. NIPS 2017: 6681-6690 - [i4]Iván Castro, Cristóbal Silva, Felipe A. Tobar:
Initialising Kernel Adaptive Filters via Probabilistic Inference. CoRR abs/1707.03450 (2017) - [i3]Felipe A. Tobar:
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary. CoRR abs/1707.04236 (2017) - [i2]Felipe A. Tobar, Gonzalo Rios, Tomas Valdivia, Pablo Guerrero:
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior. CoRR abs/1707.05909 (2017) - [i1]Gabriel Parra, Felipe A. Tobar:
Spectral Mixture Kernels for Multi-Output Gaussian Processes. CoRR abs/1709.01298 (2017) - 2016
- [c9]Felipe A. Tobar, Richard E. Turner:
Modelling time series via automatic learning of basis functions. SAM 2016: 1-5 - 2015
- [j4]Felipe A. Tobar, Danilo P. Mandic:
Design of Positive-Definite Quaternion Kernels. IEEE Signal Process. Lett. 22(11): 2117-2121 (2015) - [j3]Felipe A. Tobar, Petar M. Djuric, Danilo P. Mandic:
Unsupervised State-Space Modeling Using Reproducing Kernels. IEEE Trans. Signal Process. 63(19): 5210-5221 (2015) - [c8]Felipe A. Tobar, Richard E. Turner:
Modelling of complex signals using gaussian processes. ICASSP 2015: 2209-2213 - [c7]Thiannithi Thanthawaritthisai, Felipe A. Tobar, Anthony G. Constantinides, Danilo P. Mandic:
The widely linear quaternion recursive total least squares. ICASSP 2015: 3357-3361 - [c6]Felipe A. Tobar, Thang D. Bui, Richard E. Turner:
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. NIPS 2015: 3501-3509 - 2014
- [j2]Felipe A. Tobar, Danilo P. Mandic:
Quaternion Reproducing Kernel Hilbert Spaces: Existence and Uniqueness Conditions. IEEE Trans. Inf. Theory 60(9): 5736-5749 (2014) - [j1]Felipe A. Tobar, Sun-Yuan Kung, Danilo P. Mandic:
Multikernel Least Mean Square Algorithm. IEEE Trans. Neural Networks Learn. Syst. 25(2): 265-277 (2014) - [c5]Felipe A. Tobar, Marcos E. Orchard, Danilo P. Mandic, Anthony G. Constantinides:
Estimation of financial indices volatility using a model with time-varying parameters. CIFEr 2014: 318-324 - [c4]Felipe A. Tobar, Danilo P. Mandic:
A particle filtering based kernel HMM predictor. ICASSP 2014: 7969-7973 - 2013
- [c3]Felipe A. Tobar, Danilo P. Mandic:
The quaternion kernel least squares. ICASSP 2013: 6128-6132 - 2012
- [c2]Felipe A. Tobar, Anthony Kuh, Danilo P. Mandic:
A novel augmented complex valued kernel LMS. SAM 2012: 473-476 - 2011
- [c1]Felipe A. Tobar, Luis Yacher, Rodrigo Paredes, Marcos E. Orchard:
Anomaly detection in power generation plants using similarity-based modeling and multivariate analysis. ACC 2011: 1940-1945
Coauthor Index
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last updated on 2025-03-10 21:51 CET by the dblp team
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