77 citations · 124 across the 13 of their papers we have counts for
13 papers
Correcting Model Bias with Sparse Implicit Processes
Simón Rodríguez Santana, Luis A. Ortega, Daniel Hernández-Lobato +1
Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure. Model choice may impose strong biases on the resulting predictions, which can hinder…
Deep Variational Implicit Processes
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesi…
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…
Function-space Inference with Sparse Implicit Processes
Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato
Implicit Processes (IPs) represent a flexible framework that can be used to describe a wide variety of models, from Bayesian neural networks, neural samplers and data generators to…
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…
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Real-world problems often involve the optimization of several objectives under multiple constraints. An example is the hyper-parameter tuning problem of machine learning algorithms…
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…
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of…
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li +2
A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be t…
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Thang D. Bui, José Miguel Hernández-Lobato, Yingzhen Li +2
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wid…
Convergent Expectation Propagation in Linear Models with Spike-and-slab Priors
José Miguel Hernández-Lobato, Daniel Hernández-Lobato
Exact inference in the linear regression model with spike and slab priors is often intractable. Expectation propagation (EP) can be used for approximate inference. However, the reg…