Dimensionality Reduction

Sparse PCA: a Geometric Approach JMLR 2023 Sparse PCA Beyond Covariance Thresholding COLT 2023 Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs ICML 2023 Towards a Persistence Diagram that is Robust to Noise and Varied Densities ICML 2023 A Computationally Efficient Framework for Vector Representation of Persistence Diagrams JMLR 2022 A Forward Approach for Sufficient Dimension Reduction in Binary Classification JMLR 2022 A Probabilistic Graph Coupling View of Dimension Reduction NeurIPS 2022 Artistic Style Discovery With Independent Components CVPR 2022 A Statistical Manifold Framework for Point Cloud Data ICML 2022 ATD: Augmenting CP Tensor Decomposition by Self Supervision NeurIPS 2022 Attraction-Repulsion Spectrum in Neighbor Embeddings JMLR 2022 Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources NeurIPS 2022 CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data CVPR 2022 Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs NeurIPS 2022 coVariance Neural Networks NeurIPS 2022 Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder EMNLP 2022 Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations EMNLP 2022 Distributed Sparse Multicategory Discriminant Analysis AISTATS 2022 Distributionally Robust Fair Principal Components via Geodesic Descents ICLR 2022 DP-PCA: Statistically Optimal and Differentially Private PCA NeurIPS 2022 Dynamic Manifold Learning for Land Deformation Forecasting AAAI 2022 Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs NAACL 2022 Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms AISTATS 2022 Fast and Provable Nonconvex Tensor RPCA ICML 2022 Generalized Leverage Scores: Geometric Interpretation and Applications ICML 2022