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
Uncovering Temporal Patterns in Visualizations of High-Dimensional Data
Pavlin G. Poličar, Blaž Zupan
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation
Yanhao Jin, Krishnakumar Balasubramanian, Debashis Paul
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no Libraries
Ergon Cugler de Moraes Silva
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber, Mingchen Gao
High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media
Yuya Sasaki, Jing Tao, Yulong Wang
$\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Jason Z. Kim, Nicolas Perrin-Gilbert, Erkan Narmanli, Paul Klein, Christopher R. Myers, Itai Cohen, Joshua J. Waterfall, James P. Sethna