gaussian process

PoRB-Nets: Poisson Process Radial Basis Function Networks UAI 2020 Private Outsourced Bayesian Optimization ICML 2020 Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel ICLR 2020 Quantile Propagation for Wasserstein-Approximate Gaussian Processes NeurIPS 2020 Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation IJCAI 2020 Randomly Projected Additive Gaussian Processes for Regression ICML 2020 Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization NeurIPS 2020 Regret Bound for Safe Gaussian Process Bandit Optimization L4DC 2020 Regularized Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2020 Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes NeurIPS 2020 Robust Regression for Safe Exploration in Control L4DC 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase ICML 2020 Scalable Nonparametric Factorization for High-Order Interaction Events AISTATS 2020 Scalable Uncertainty for Computer Vision With Functional Variational Inference CVPR 2020 Self-Modulating Nonparametric Event-Tensor Factorization ICML 2020 Semi-parametric Learning of Structured Temporal Point Processes JMLR 2020 Sensor Placement for Spatial Gaussian Processes with Integral Observations UAI 2020 Sparse Gaussian Processes with Spherical Harmonic Features ICML 2020 Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning NeurIPS 2020 Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks ICML 2020 Stable behaviour of infinitely wide deep neural networks AISTATS 2020 State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes ICML 2020 Stationary Activations for Uncertainty Calibration in Deep Learning NeurIPS 2020 Stochastic Deep Gaussian Processes over Graphs NeurIPS 2020