Dimensionality Reduction

Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction JMLR 2024 A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models NeurIPS 2024 Black-Box k-to-1-PCA Reductions: Theory and Applications COLT 2024 Capturing the denoising effect of PCA via compression ratio NeurIPS 2024 Consistency of Dictionary-Based Manifold Learning AISTATS 2024 Contrastive dimension reduction: when and how? NeurIPS 2024 DiffRed: Dimensionality reduction guided by stable rank AISTATS 2024 Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis NeurIPS 2024 Efficient Centroid-Linkage Clustering NeurIPS 2024 Efficient Leverage Score Sampling for Tensor Train Decomposition NeurIPS 2024 ESPACE: Dimensionality Reduction of Activations for Model Compression NeurIPS 2024 Evaluating Dynamic Topic Models ACL 2024 From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data JMLR 2024 Generalized Eigenvalue Problems with Generative Priors NeurIPS 2024 Implicit Regularization in Deep Tucker Factorization: Low-Rankness via Structured Sparsity AISTATS 2024 Learned Transformer Position Embeddings Have a Low-Dimensional Structure ACL 2024 LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation AAAI 2024 Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering AAAI 2024 Manifold-Based Verbalizer Space Re-embedding for Tuning-Free Prompt-Based Classification AAAI 2024 MAP IT to Visualize Representations ICLR 2024 Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions EMNLP 2024 Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems NeurIPS 2024 Oja's Algorithm for Streaming Sparse PCA NeurIPS 2024 On Sparse Canonical Correlation Analysis NeurIPS 2024 On Sufficient Graphical Models JMLR 2024