High Dimensional
High-dimensional data analysis focuses on extracting meaningful information and building predictive models from datasets with numerous variables, often exceeding the number of observations. Current research emphasizes developing computationally efficient algorithms, such as stochastic gradient descent and its variants, and novel model architectures like graph neural networks and deep learning approaches tailored to handle the unique challenges posed by high dimensionality, including issues of sparsity and missing data. These advancements are crucial for addressing complex problems across diverse fields, including scientific modeling, robotics, and financial risk assessment, where high-dimensional data are increasingly prevalent.
Papers
nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model
Haifan Gong, Luoyao Kang, Yitao Wang, Xiang Wan, Haofeng Li
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
Lam Ngo, Huong Ha, Jeffrey Chan, Vu Nguyen, Hongyu Zhang
Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian Optimization
Zhitong Xu, Haitao Wang, Jeff M Phillips, Shandian Zhe
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, Titouan Vayer
Vanilla Bayesian Optimization Performs Great in High Dimensions
Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi