gaussian process

Bayesian Graphical Models for Multivariate Functional Data JMLR 2016 Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models JMLR 2016 Bayesian Matrix Factorization with Non-Random Missing Data using Informative Gaussian Process Priors and Soft Evidences PGM 2016 Bayesian Policy Gradient and Actor-Critic Algorithms JMLR 2016 Chained Gaussian Processes AISTATS 2016 Compressed Gaussian Process for Manifold Regression JMLR 2016 Computationally Efficient Bayesian Learning of Gaussian Process State Space Models AISTATS 2016 Deep Kernel Learning AISTATS 2016 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ICML 2016 e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem JMLR 2016 Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods NeurIPS 2016 Extended and Unscented Kitchen Sinks ICML 2016 Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations NeurIPS 2016 Gaussian Processes for Survival Analysis NeurIPS 2016 Gaussian process nonparametric tensor estimator and its minimax optimality ICML 2016 High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models AISTATS 2016 Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs COLING 2016 Latent Point Process Allocation AISTATS 2016 Minimum Regret Search for Single- and Multi-Task Optimization ICML 2016 Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs RSS 2016 Observational-Interventional Priors for Dose-Response Learning NeurIPS 2016 Optimization as Estimation with Gaussian Processes in Bandit Settings AISTATS 2016 Pareto Frontier Learning with Expensive Correlated Objectives ICML 2016 Probabilistic Approximate Least-Squares AISTATS 2016 Probabilistic Linear Multistep Methods NeurIPS 2016