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Found 30 papers, 12 papers with code
Deep Transformed Gaussian Processes
In this work, we propose a generalization of TGPs named Deep Transformed Gaussian Processes (DTGPs), which follows the trend of concatenating layers of stochastic processes.
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Specifically, its training cost is independent of the number of training points.
Correcting Model Bias with Sparse Implicit Processes
Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure.
Deep Variational Implicit Processes
This generalization is similar to that of deep GPs over GPs, but it is more flexible due to the use of IPs as the prior distribution over the latent functions.
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
ETGPs exploit the recently proposed Transformed Gaussian Process (TGP), a stochastic process specified by transforming a Gaussian Process using an invertible transformation.
Gaussian Processes for Missing Value Imputation
Sparse GPs can be used to compute a predictive distribution for missing data.
Inference over radiative transfer models using variational and expectation maximization methods
Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to model and understand the Earth system, as well as to estimate the parameters that describe the status of the Earth from satellite observations by inverse modeling.
Function-space Inference with Sparse Implicit Processes
The result is a scalable method for approximate inference with IPs that can tune the prior IP parameters to the data, and that provides accurate non-Gaussian predictive distributions.
Input Dependent Sparse Gaussian Processes
For this, we use a neural network that receives the observed data as an input and outputs the inducing points locations and the parameters of $q$.
Activation-level uncertainty in deep neural networks
Current approaches for uncertainty estimation in deep learning often produce too confident results.
Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints
MESMOC+ is also competitive with other information-based methods for constrained multi-objective Bayesian optimization, but it is significantly faster.
Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
This article introduces PPESMOC, Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based batch method for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints.
Multi-class Gaussian Process Classification with Noisy Inputs
The results obtained show that, although the classification error is similar across methods, the predictive distribution of the proposed methods is better, in terms of the test log-likelihood, than the predictive distribution of a classifier based on GPs that ignores input noise.
Adversarial $α$-divergence Minimization for Bayesian Approximate Inference
Estimating the uncertainty in the predictions is a critical aspect with important applications, and one method to obtain this information is following a Bayesian approach to estimate a posterior distribution on the model parameters.
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
We show that the parameters found by a BO method outperform those found by a random search strategy and the expert recommendation.
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
We show that this can lead to problems in the optimization process and describe a more principled approach to account for input variables that are categorical or integer-valued.
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Furthermore, extra assumptions in the approximate inference process make the memory cost independent of $N$.
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
We show that this can lead to problems in the optimization process and describe a more principled approach to account for input variables that are integer-valued.
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints.
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers.
Predictive Entropy Search for Multi-objective Bayesian Optimization
The results show that PESMO produces better recommendations with a smaller number of evaluations of the objectives, and that a decoupled evaluation can lead to improvements in performance, particularly when the number of objectives is large.
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers.
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP).
Black-box $α$-divergence Minimization
Black-box alpha (BB-$\alpha$) is a new approximate inference method based on the minimization of $\alpha$-divergences.
Scalable Gaussian Process Classification via Expectation Propagation
Variational methods have been recently considered for scaling the training process of Gaussian process classifiers to large datasets.
Non-linear Causal Inference using Gaussianity Measures
The problem of non-linear causal inference is addressed by performing an embedding in an expanded feature space, in which the relation between causes and effects can be assumed to be linear.
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC sigmoid likelihood function.
Learning Feature Selection Dependencies in Multi-task Learning
Because the process of estimating feature selection dependencies may suffer from over-fitting in the model proposed, additional data from a multi-task learning scenario are considered for induction.
Gaussian Process Conditional Copulas with Applications to Financial Time Series
The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series.
Robust Multi-Class Gaussian Process Classification
When no noise is injected in the labels, RMGPC still performs equal or better than the other methods.
