Gaussian Processes

Scaling Gaussian Process Regression with Derivatives NeurIPS 2018 State Space Gaussian Processes with Non-Gaussian Likelihood ICML 2018 Structured Gaussian Processes with Twin Multiple Kernel Learning ACML 2018 Variational Fourier Features for Gaussian Processes JMLR 2018 Variational Learning on Aggregate Outputs with Gaussian Processes NeurIPS 2018 Wrapped Gaussian Process Regression on Riemannian Manifolds CVPR 2018 A Covariance Matrix Adaptation Evolution Strategy for Direct Policy Search in Reproducing Kernel Hilbert Space ACML 2017 Active Incremental Learning of Robot Movement Primitives CoRL 2017 A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings ACML 2017 A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing IJCAI 2017 A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation JMLR 2017 Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes NeurIPS 2017 Bayesian Learning of Dynamic Multilayer Networks JMLR 2017 Cross-Spectral Factor Analysis NeurIPS 2017 Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs NeurIPS 2017 Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks NeurIPS 2017 Doubly Stochastic Variational Inference for Deep Gaussian Processes NeurIPS 2017 DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output SemEval 2017 Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models NeurIPS 2017 Extending Model-based Policy Gradients for Robots in Heteroscedastic Environments CoRL 2017 Gaussian process based nonlinear latent structure discovery in multivariate spike train data NeurIPS 2017 GP CaKe: Effective brain connectivity with causal kernels NeurIPS 2017 Gray-box Inference for Structured Gaussian Process Models AISTATS 2017 Identification of Gaussian Process State Space Models NeurIPS 2017 Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes NeurIPS 2017