sparse approximation

Dynamic Similarity Graph Construction with Kernel Density Estimation ICML 2025 KernelMatmul: Scaling Gaussian Processes to Large Time Series AAAI 2025 Understanding Multi-layered Transmission Matrices CVPR 2025 Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees JMLR 2024 Revisiting Kernel Attention with Correlated Gaussian Process Representation UAI 2024 Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes NeurIPS 2024 Actually Sparse Variational Gaussian Processes AISTATS 2023 A Novel Integer Linear Programming Approach for Global L0 Minimization JMLR 2023 Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time ICML 2023 Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification ICML 2023 Fast Approximation of Similarity Graphs with Kernel Density Estimation NeurIPS 2023 Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes ICML 2023 Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients ICML 2023 LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation ICML 2023 Scalable nonparametric Bayesian learning for dynamic velocity fields UAI 2023 Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization ICML 2023 Additive Gaussian Processes Revisited ICML 2022 Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning ICML 2022 Input Dependent Sparse Gaussian Processes ICML 2022 Log-Linear-Time Gaussian Processes Using Binary Tree Kernels NeurIPS 2022 Sparse Additive Gaussian Process Regression JMLR 2022 Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS 2022 Tighter Sparse Approximation Bounds for ReLU Neural Networks ICLR 2022 Variational nearest neighbor Gaussian process ICML 2022 Active Learning with Maximum Margin Sparse Gaussian Processes AISTATS 2021