sparse approximation

Differentially Private Regression and Classification with Sparse Gaussian Processes JMLR 2021 Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior AAAI 2021 Practical Real Time Recurrent Learning with a Sparse Approximation ICLR 2021 Scalable Gaussian Process Variational Autoencoders AISTATS 2021 Scalable Thompson Sampling using Sparse Gaussian Process Models NeurIPS 2021 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data ICML 2021 Sparse within Sparse Gaussian Processes using Neighbor Information ICML 2021 Spatio-Temporal Variational Gaussian Processes NeurIPS 2021 Subset-of-data variational inference for deep Gaussian-processes regression UAI 2021 Value-aware Approximate Attention EMNLP 2021 Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances ICML 2020 Dataless Model Selection With the Deep Frame Potential CVPR 2020 Doubly Sparse Variational Gaussian Processes AISTATS 2020 Efficiently sampling functions from Gaussian process posteriors ICML 2020 Estimating Model Uncertainty of Neural Networks in Sparse Information Form ICML 2020 Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes NeurIPS 2020 Sparse Gaussian Processes with Spherical Harmonic Features ICML 2020 Bayesian Batch Active Learning as Sparse Subset Approximation NeurIPS 2019 Efficiently Learning Fourier Sparse Set Functions NeurIPS 2019 Harmonizable mixture kernels with variational Fourier features AISTATS 2019 Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection JMLR 2018 Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds NeurIPS 2018 Nonrigid Points Alignment with Soft-weighted Selection IJCAI 2018 Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation CoRL 2018 Variational Fourier Features for Gaussian Processes JMLR 2018