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02/24/2023
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Pre-trained deep neural networks can be adapted to perform uncertainty e...
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07/21/2022
Correcting Model Bias with Sparse Implicit Processes
Model selection in machine learning (ML) is a crucial part of the Bayesi...
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06/14/2022
Deep Variational Implicit Processes
Implicit processes (IPs) are a generalization of Gaussian processes (GPs...
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05/30/2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
This work introduces the Efficient Transformed Gaussian Process (ETGP), ...
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04/10/2022
Gaussian Processes for Missing Value Imputation
Missing values are common in many real-life datasets. However, most of t...
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04/07/2022
Inference over radiative transfer models using variational and expectation maximization methods
Earth observation from satellites offers the possibility to monitor our ...
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10/14/2021
Sparse Implicit Processes for Approximate Inference
Implicit Processes (IPs) are flexible priors that can describe models su...
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07/15/2021
Input Dependent Sparse Gaussian Processes
Gaussian Processes (GPs) are Bayesian models that provide uncertainty es...
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04/01/2020
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
Real-world problems often involve the optimization of several objectives...
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01/28/2020
Multi-class Gaussian Process Classification with Noisy Inputs
It is a common practice in the supervised machine learning community to ...
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09/13/2019
Adversarial α-divergence Minimization for Bayesian Approximate Inference
Neural networks are popular models for regression. They are often traine...
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06/28/2018
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
The PC algorithm is a popular method for learning the structure of Gauss...
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05/09/2018
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Bayesian Optimization (BO) methods are useful for optimizing functions t...
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06/22/2017
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
This paper describes an expectation propagation (EP) method for multi-cl...
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06/12/2017
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Bayesian optimization (BO) methods are useful for optimizing functions t...
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09/05/2016
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
This work presents PESMOC, Predictive Entropy Search for Multi-objective...
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02/12/2016
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisati...
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11/17/2015
Predictive Entropy Search for Multi-objective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of mu...
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11/11/2015
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisati...
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11/10/2015
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recent...
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11/10/2015
Black-box α-divergence Minimization
Black-box alpha (BB-α) is a new approximate inference method based on th...
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07/01/2014
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
The learning with privileged information setting has recently attracted ...
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07/01/2013
Gaussian Process Conditional Copulas with Applications to Financial Time Series
The estimation of dependencies between multiple variables is a central p...
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12/10/2011