Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Understanding Probabilistic Sparse Gaussian Process Approximations
A unifying view of sparse approximate Gaussian process regression
Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox
Gaussian Processes for Machine Learning
Sparse Gaussian Processes using Pseudo-inputs
Warped Gaussian Processes
Fast Bayesian inference for nonconjugate Gaussian process regression
Gaussian Process Networks
Explicit Approximations of the Gaussian Kernel
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Projected Subgradient Methods for Learning Sparse Gaussians
Deep Multi-fidelity Gaussian Processes
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
Gaussian Processes Over Graphs
Probabilistic Programming with Gaussian Process Memoization
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
Automated Variational Inference for Gaussian Process Models
Spike and Slab Gaussian Process Latent Variable Models
Prediction With Gaussian Processes: From Linear Regression To Linear Prediction And Beyond
Student-t Processes as Alternatives to Gaussian Processes
An improved valence-arousal emotion space for video affective content representation and recognition
Statistical primer: sample size and power calculations—why, when and how?†
JBOSS: The Evolution of Professional Open Source Software
Stochastic Variational Inference
Data Breaches and Identity Theft: When is Mandatory Disclosure Optimal?