matrix approximation

Robust Subspace Approximation in a Stream NeurIPS 2018 Subspace Embedding and Linear Regression with Orlicz Norm ICML 2018 Approximation Algorithms for ℓ_0-Low Rank Approximation NeurIPS 2017 A Simple Language Model based on PMI Matrix Approximations EMNLP 2017 Distributed Adaptive Sampling for Kernel Matrix Approximation AISTATS 2017 Faster Principal Component Regression and Stable Matrix Chebyshev Approximation ICML 2017 Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data NeurIPS 2017 Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? NeurIPS 2017 Memory Efficient Kernel Approximation JMLR 2017 Mixture-Rank Matrix Approximation for Collaborative Filtering NeurIPS 2017 Recursive Sampling for the Nystrom Method NeurIPS 2017 Storage Fit Learning with Unlabeled Data IJCAI 2017 Fast DPP Sampling for Nystrom with Application to Kernel Methods ICML 2016 Faster Eigenvector Computation via Shift-and-Invert Preconditioning ICML 2016 Principal Component Projection Without Principal Component Analysis ICML 2016 Revisiting the Nyström Method for Improved Large-scale Machine Learning JMLR 2016 SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions JMLR 2016 Subspace Learning with Partial Information JMLR 2016 Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition JMLR 2016 A Statistical Perspective on Algorithmic Leveraging JMLR 2015 Column Selection via Adaptive Sampling NeurIPS 2015 Efficient SDP Inference for Fully-Connected CRFs Based on Low-Rank Decomposition CVPR 2015 Fast Randomized Singular Value Thresholding for Nuclear Norm Minimization CVPR 2015 Online PCA with Spectral Bounds COLT 2015 Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares ICML 2015