gaussian process

Bayesian Image Classification with Deep Convolutional Gaussian Processes AISTATS 2020 Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances ICML 2020 Bayesian Optimisation over Multiple Continuous and Categorical Inputs ICML 2020 Bayesian Optimization of Risk Measures NeurIPS 2020 Bayesian Optimization using Pseudo-Points IJCAI 2020 Bayesian Probabilistic Numerical Integration with Tree-Based Models NeurIPS 2020 Black-Box Inference for Non-Linear Latent Force Models AISTATS 2020 BOSS: Bayesian Optimization over String Spaces NeurIPS 2020 BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization NeurIPS 2020 Causal Inference using Gaussian Processes with Structured Latent Confounders ICML 2020 ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations AISTATS 2020 Conjugate Gradients for Kernel Machines JMLR 2020 Continual Deep Learning by Functional Regularisation of Memorable Past NeurIPS 2020 Convergence of Sparse Variational Inference in Gaussian Processes Regression JMLR 2020 Corruption-Tolerant Gaussian Process Bandit Optimization AISTATS 2020 Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare AAAI 2020 Deep Rao-Blackwellised Particle Filters for Time Series Forecasting NeurIPS 2020 Deep Structured Mixtures of Gaussian Processes AISTATS 2020 Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems ICML 2020 Distributionally Ambiguous Optimization for Batch Bayesian Optimization JMLR 2020 Doubly Sparse Variational Gaussian Processes AISTATS 2020 Dynamic Embedding on Textual Networks via a Gaussian Process AAAI 2020 Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables JMLR 2020 Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions L4DC 2020 Efficiently sampling functions from Gaussian process posteriors ICML 2020