dimensionality reduction

Canonical normalizing flows for manifold learning NeurIPS 2023 Computing Approximate ℓ_p Sensitivities NeurIPS 2023 Contrastive Learning with the Feature Reconstruction Amplifier AAAI 2023 Density estimation on low-dimensional manifolds: an inflation-deflation approach JMLR 2023 Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon Forecasting AISTATS 2023 Dimensionality Reduction and Wasserstein Stability for Kernel Regression JMLR 2023 Dimensionality Reduction for General KDE Mode Finding ICML 2023 Dimensionality Reduction for Machine Learning-based Argument Mining EMNLP 2023 Dimensionality-Varying Diffusion Process CVPR 2023 Efficient fair PCA for fair representation learning AISTATS 2023 Error in the Euclidean Preference Model IJCAI 2023 Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms ICML 2023 Fair Neighbor Embedding ICML 2023 Featured Graph Coarsening with Similarity Guarantees ICML 2023 From t-SNE to UMAP with contrastive learning ICLR 2023 Geometric Autoencoders - What You See is What You Decode ICML 2023 Insights into Ordinal Embedding Algorithms: A Systematic Evaluation JMLR 2023 Isometric Manifold Learning Using Hierarchical Flow AAAI 2023 Kernelized Diffusion Maps COLT 2023 Learning 3D Human Pose Estimation From Dozens of Datasets Using a Geometry-Aware Autoencoder To Bridge Between Skeleton Formats WACV 2023 Learning topology-preserving data representations ICLR 2023 Matrix Compression via Randomized Low Rank and Low Precision Factorization NeurIPS 2023 MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability NeurIPS 2023 Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data JMLR 2023 On Coresets for Clustering in Small Dimensional Euclidean spaces ICML 2023