Gaussian Processes

Constant-Time Predictive Distributions for Gaussian Processes ICML 2018 Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches ICML 2018 Differentiable Compositional Kernel Learning for Gaussian Processes ICML 2018 Differentially Private Database Release via Kernel Mean Embeddings ICML 2018 Differential networking with path weights in Gaussian trees PGM 2018 Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification NeurIPS 2018 Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes JMLR 2018 Feature-correlation-aware Gaussian Process Latent Variable Model ACML 2018 Gaussian Process Neural Networks for Speech Recognition Interspeech 2018 Gaussian Process Prior Variational Autoencoders NeurIPS 2018 GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration NeurIPS 2018 Heterogeneous Multi-output Gaussian Process Prediction NeurIPS 2018 How Deep Are Deep Gaussian Processes? JMLR 2018 Inferring Latent Velocities from Weather Radar Data using Gaussian Processes NeurIPS 2018 Infinite-Horizon Gaussian Processes NeurIPS 2018 Large-Scale Cox Process Inference using Variational Fourier Features ICML 2018 Learning a latent manifold of odor representations from neural responses in piriform cortex NeurIPS 2018 Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds NeurIPS 2018 Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design ICML 2018 Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models AISTATS 2018 Patchwork Kriging for Large-scale Gaussian Process Regression JMLR 2018 Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior NeurIPS 2018 Safe Active Learning for Time-Series Modeling with Gaussian Processes NeurIPS 2018 Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition AISTATS 2018 Scalable Generalized Dynamic Topic Models AISTATS 2018