sparse principal component analysis

Learning to Rank: How GNNs Solve Max-Clique and Sparse PCA AAAI 2026 ADMM for Nonconvex Optimization under Minimal Continuity Assumption ICLR 2025 Efficient Sparse PCA via Block-Diagonalization ICLR 2025 Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression COLT 2024 Learning with a linear loss function: excess risk and estimation bounds for ERM, minmax MOM and their regularized versions with applications to robustness in sparse PCA. JMLR 2024 Oja's Algorithm for Streaming Sparse PCA NeurIPS 2024 Support Recovery in Sparse PCA with General Missing Data UAI 2024 Optimizing Spca-based Continual Learning: A Theoretical Approach ICLR 2023 Sparse PCA: a Geometric Approach JMLR 2023 Sparse PCA Beyond Covariance Thresholding COLT 2023 Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms AISTATS 2022 Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality JMLR 2022 Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis NeurIPS 2022 Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap NeurIPS 2022 Support Recovery in Sparse PCA with Incomplete Data NeurIPS 2022 Average-Case Communication Complexity of Statistical Problems COLT 2021 Statistical Query Algorithms and Low Degree Tests Are Almost Equivalent COLT 2021 The Complexity of Sparse Tensor PCA NeurIPS 2021 Free Energy Wells and Overlap Gap Property in Sparse PCA COLT 2020 Upper bounds for Model-Free Row-Sparse Principal Component Analysis ICML 2020 A Kernel Random Matrix-Based Approach for Sparse PCA ICLR 2019 Optimal Average-Case Reductions to Sparse PCA: From Weak Assumptions to Strong Hardness COLT 2019 Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering NeurIPS 2019 Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems AISTATS 2018 Sparse PCA from Sparse Linear Regression NeurIPS 2018