- A Metamodeling Method Using Dynamic Kriging and Sequential Sampling
- Scalable Gaussian Process Regression Using Deep Neural Networks
- VARIATIONAL AUTO-ENCODED DEEP GAUSSIAN PROCESSES
- Deep Kernel Learning
- Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
- Deep Gaussian Processes for Regression using Approximate Expectation Propagation
- Warped Gaussian Processes Occupancy Mapping with Uncertain Inputs
- Introduction to Gaussian Process (David J.C. Mackay)
- A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
- Short-Term Wind Power Forecasting Using Gaussian Processes
- Online Kernel Selection for Bayesian Reinforcement Learning
- The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors
- PREDICTION WITH GAUSSIAN PROCESSES: FROM LINEAR REGRESSION TO LINEAR PREDICTION AND BEYOND
- Gaussian Process for Machine Learning
- Function factorization using warped Gaussian processes (slides)
- Warped Gaussian processes and derivative-based sequential design for functions with heterogeneous variations
- Training Deep Gaussian Processes with Sampling
- Multi-Task Warped Gaussian Process for Personalized Age Estimation
- Modelling and Control of Nonlinear Systems using Gaussian Processes with Partial Model Information
- ANOVA kernels and RKHS of zero mean functions model-based sensitivity analysis
- ADDITIVE COVARIANCE KERNELS FOR HIGH-DIMENSIONAL GAUSSIAN PROCESS MODELING
- Argument-wise invariant kernels for the approximation of invariant functions
- Deep Gaussian Processes – Neil D. Lawrence (slides)
- Deep Gaussian Processes- Andreas C. Damianou and Neil D. Lawrence (slides)
- Deep Gaussian Processes – AISTATS2013
- Deep Gaussian Processes Andreas C. Damianou and Neil D. Lawrence AISTATS 2013
- Deep Gaussian Processes – Andreas C. Damianou Neil D. Lawrence
- Deep Gaussian Processes – Neil D. Lawrence (slides)
- Feature representation with Deep Gaussian processes – Andreas Damianou (slides)
- Learning and Inference with Gaussian Processes An Overview of Gaussian Processes with some state of the art applications – Neil D. Lawrence (slides)
- The Gaussian Process Latent Variable Model Neil D. Lawrence (slides)
- On ANOVA decompositions of kernels and Gaussian random field paths
- Introduction to Kriging using R and JMP – Nicolas Durrande (slides)
- Gaussian process models for periodicity detection – Nicolas Durrande, James Hensman, Magnus Rattray, Neil D. Lawrence
- Some properties of nested Kriging predictors
- Inverse Reinforcement Learning via Deep Gaussian Process
- Off-Policy Reinforcement Learning with Gaussian Processes
- Sample Efficient Reinforcement Learning with Gaussian Processes
- Regression and Classification Using Gaussian Process Priors
- Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
- Deep Gaussian Processes for Large Datasets
- Inverse Reinforcement Learning via Deep Gaussian Process
- Multi-fdelity stochastic modeling with Gaussian processes: Learning and optimization under uncertainty
- Why Does Unsupervised Pre-training Help Deep Learning?
- Gaussian Process Kernels for Pattern Discovery and Extrapolation
- Bagging for Gaussian Process Regression
- THE VARIATIONAL GAUSSIAN PROCESS
- Manifold Gaussian Processes for Regression
- Scalable Variational Gaussian Process Classification
- Scalable Gaussian Process Regression Using Deep Neural Networks
- Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation (slides)
- Deep Gaussian Processes for Regression using Approximate Expectation Propagation
- Uncertainty Quantification Using Deep Gaussian Processes
- Gaussian processes autoencoder for dimensionality reduction
- How priors of initial hyperparameters affect Gaussian process regression models
- Environmental Modeling Framework using Stacked Gaussian Processes
- Practical Bayesian Optimization of Machine Learning Algorithms
- Gaussian processes
- The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors
- A Tutorial on Gaussian Processes
- An efficient methodology for modeling complex computer codes with Gaussian processes
- Constrained Gaussian process modeling – slides
- GAUSSIAN PROCESS MODELING WITH INEQUALITY CONSTRAINTS
- Practical Recommendations for Gradient-Based Training of Deep Architectures
- Gaussian process Metamodeling applied to a Circulation Control Wing
- Metamodeling with Gaussian Processes
- Statistical Meta-Modeling for Complex System Simulations: Kriging, Alternatives and Design – slides
- Metamodel-based sensitivity analysis: Polynomial chaos expansions and Gaussian processes
- Sequential search strategies based on kriging
- A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators
- An efficient methodology for modeling complex computer codes with Gaussian processes
- CALCULATIONS OF SOBOL INDICES FOR THE GAUSSIAN PROCESS METAMODEL
- A Unifying Review of Linear Gaussian Models
- Analysis of Computer Experiments Using Penalized Likelihood in Gaussian Kriging Models
- Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output
- Bayesian calibration of numerical models using Gaussian processes – slides
- Non-linear Matrix Factorization with Gaussian Processes – slides
- Non-linear Matrix Factorization with Gaussian Processes -paper
- Non-linear Matrix Factorization with Gaussian Processes- slides
- A Study on Polynomial Regression and Gaussian Process Global Surrogate Model in Hierarchical Surrogate-Assisted Evolutionary Algorithm
- Cross-Validation Estimations of Hyper-Parameters of Gaussian Processes with Inequality Constraints
- KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on Kriging
- Kriging of financial term-structures
- A supermartingale approach to Gaussian process based sequential design of experiments
- Local and global sparse Gaussian process approximations
- Gaussian processes -Richard A. Davis
- BOUNDED GAUSSIAN PROCESS REGRESSION
- Warped Gaussian Processes
- Function factorization using warped Gaussian processes
- Introduction to Gaussian Processes – slides – Iain Murray
- Function factorization using warped Gaussian processes – slides – Mikkel N. Schmidt
- Compressed Gaussian Process for Manifold Regression
- Compressed Gaussian Process Manifold Regression
- Distributed Gaussian Processes
- Gaussian Processes for Data-Efficient Learning in Robotics and Control
- Doubly Stochastic Variational Inference for Deep Gaussian Processes
- A Technique for Use of Gaussian Processes in Advanced Meta-Modeling
- Sparse Greedy Gaussian Process Regression
- Gaussian Processes for Machine Learning (GPML) Toolbox
- Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
- Sparse Gaussian Processes using Pseudo-inputs
- Approximate Inference for Robust Gaussian Process Regression
- Multi-output local Gaussian process regression: Applications to uncertainty quantification
- New Directions for Learning with Kernels and Gaussian Processes
- Warped Gaussian Processes Occupancy Mapping with Uncertain Inputs
- Approximations for Binary Gaussian Process Classification
- Introduction to Uncertainty Quantification and Gaussian Processes – slides
- Gaussian Processes for Classification- slides
- Gaussian Processes for Data-Efficient Learning in Robotics and Control
- Gaussian Process Approximations of Stochastic Differential Equations
- Multi-class Semi-supervised Learning With The ǫ-truncated Multinomial Probit Gaussian Process
- Learning RoboCup-Keepaway with Kernels
- Salient Point and Scale Detection by Minimum Likelihood
- Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology
- Distributed Gaussian Processes
- A review on Gaussian Process Latent Variable Models
- How to choose the covariance for Gaussian process regression independently of the basis
- Some Comparisons for Gaussian Processes
- Learning curves for Gaussian processes
- Gaussian Processes in Reinforcement Learning
- Warped Gaussian Processes
- Reinforcement learning with Gaussian processes
- Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classi cation
- Beyond Gaussian Processes: On the Distributions of Infinite Networks
- A tutorial on Gaussian process regression with a focus on exploration-exploitation scenarios
- Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
- Gaussian Process Kernels for Pattern Discovery and Extrapolation – slides
- AN ADDITIVE GLOBAL AND LOCAL GAUSSIAN PROCESS MODEL FOR LARGE DATA SETS
- Gaussian Process Latent Variable Models for Dimensionality Reduction and Time Series Modeling
- Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
- Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units
- EXTRINSIC GAUSSIAN PROCESSES FOR REGRESSION AND CLASSIFICATION ON MANIFOLDS