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Gaussian Processes

568 directly classified papers

Papers per year

Papers

Deep Gaussian Markov Random Fields ICML 2020
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances ICML 2020
Multi-Speaker Text-to-Speech Synthesis Using Deep Gaussian Processes INTERSPEECH 2020
Continual Deep Learning by Functional Regularisation of Memorable Past NIPS 2020
Stochastic Deep Gaussian Processes over Graphs NIPS 2020
Multi-task Causal Learning with Gaussian Processes NIPS 2020
Neuronal Gaussian Process Regression NIPS 2020
Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract) IJCAI 2020
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data NIPS 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties NIPS 2020
Influenza Forecasting Framework based on Gaussian Processes ICML 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning NIPS 2020
On Infinite-Width Hypernetworks NIPS 2020
Variational Bayesian Monte Carlo with Noisy Likelihoods NIPS 2020
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes NIPS 2020
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation IJCAI 2020
Sensor Placement for Spatial Gaussian Processes with Integral Observations UAI 2020
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights NIPS 2020
Interpretable Deep Gaussian Processes with Moments AISTATS 2020
Multi-task Learning for Aggregated Data using Gaussian Processes NIPS 2019
Learning from demonstration with model-based Gaussian process CORL 2019
A Learnable Safety Measure CORL 2019
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions UAI 2019
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks UAI 2019
Periodic Kernel Approximation by Index Set Fourier Series Features UAI 2019