| Compositionally-warped Gaussian processes G Rios, F Tobar Neural Networks 118, 235-246, 2019 | 54 | 2019 |
| Bayesian learning with Wasserstein barycenters J Backhoff-Veraguas, J Fontbona, G Rios, F Tobar ESAIM: Probability and Statistics 26, 436-472, 2022 | 26 | 2022 |
| 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 |
| A Practical Query Language for Graph DBs. R Angles, P Barceló, G Rios AMW, 2013 | 20 | 2013 |
| Stochastic gradient descent in Wasserstein space J Backhoff-Veraguas, J Fontbona, G Rios, F Tobar arXiv preprint arXiv:2201.04232, 2022 | 14 | 2022 |
| Series de tiempo R Gonzalo Santiago: Univer, 2008 | 8 | 2008 |
| Recovering latent signals from a mixture of measurements using a Gaussian process prior F Tobar, G Rios, T Valdivia, P Guerrero IEEE Signal Processing Letters 24 (2), 231-235, 2016 | 6 | 2016 |
| Transport gaussian processes for regression G Rios arXiv preprint arXiv:2001.11473, 2020 | 5 | 2020 |
| Diseño e implementación de un lenguaje de consulta para bases de datos de grafos GA Ríos Díaz Universidad de Chile, 2013 | 1 | 2013 |
| Wasserstein Barycenters for Bayesian Learning: Technical Report G Rios | | 2020 |
| Contributions to bayesian machine learning via transport maps GA Ríos Díaz Universidad de Chile, 2020 | | 2020 |
Felipe TobarUniversidad de ChileEmail verificata su dim.uchile.cl
