4 citations · 10 across the 4 of their papers we have counts for
5 papers
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
Juan Maroñas, Daniel Hernández-Lobato
This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the…
Gaussian Processes for Missing Value Imputation
Bahram Jafrasteh, Daniel Hernández-Lobato, Simón Pedro Lubián-López +1
Missing values are common in many real-life datasets. However, most of the current machine learning methods can not handle missing values. This means that they should be imputed be…
Input Dependent Sparse Gaussian Processes
Bahram Jafrasteh, Carlos Villacampa-Calvo, Daniel Hernández-Lobato
Gaussian Processes (GPs) are Bayesian models that provide uncertainty estimates associated to the predictions made. They are also very flexible due to their non-parametric nature.…
Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints
Daniel Fernández-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
We present MESMOC+, an improved version of Max-value Entropy search for Multi-Objective Bayesian optimization with Constraints (MESMOC). MESMOC+ can be used to solve constrained mu…
Multi-class Gaussian Process Classification with Noisy Inputs
Carlos Villacampa-Calvo, Bryan Zaldivar, Eduardo C. Garrido-Merchán +1
It is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are comm…