
Input Dependent Sparse Gaussian Processes
Gaussian Processes (GPs) are Bayesian models that provide uncertainty es...
read it

Parallel Predictive Entropy Search for Multiobjective Bayesian Optimization with Constraints
Realworld problems often involve the optimization of several objectives...
read it

Multiclass Gaussian Process Classification with Noisy Inputs
It is a common practice in the supervised machine learning community to ...
read it

Adversarial αdivergence Minimization for Bayesian Approximate Inference
Neural networks are popular models for regression. They are often traine...
read it

Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
The PC algorithm is a popular method for learning the structure of Gauss...
read it

Dealing with Categorical and Integervalued Variables in Bayesian Optimization with Gaussian Processes
Bayesian Optimization (BO) methods are useful for optimizing functions t...
read it

Scalable MultiClass Gaussian Process Classification using Expectation Propagation
This paper describes an expectation propagation (EP) method for multicl...
read it

Dealing with Integervalued Variables in Bayesian Optimization with Gaussian Processes
Bayesian optimization (BO) methods are useful for optimizing functions t...
read it

Predictive Entropy Search for Multiobjective Bayesian Optimization with Constraints
This work presents PESMOC, Predictive Entropy Search for Multiobjective...
read it

Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
read it

Predictive Entropy Search for Multiobjective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of mu...
read it

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
read it

Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recent...
read it

Blackbox αdivergence Minimization
Blackbox alpha (BBα) is a new approximate inference method based on th...
read it

Mind the Nuisance: Gaussian Process Classification using Privileged Noise
The learning with privileged information setting has recently attracted ...
read it

Gaussian Process Conditional Copulas with Applications to Financial Time Series
The estimation of dependencies between multiple variables is a central p...
read it

Convergent Expectation Propagation in Linear Models with Spikeandslab Priors
Exact inference in the linear regression model with spike and slab prior...
read it
Daniel HernándezLobato
is this you? claim profile
Lecturer of computer science at Universidad Autónoma de Madrid, Computer Science department. Since January, 2014.