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
CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression
Masahiro Kato, Masaaki Imaizumi
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
Tsai Hor Chan, Kin Wai Lau, Jiajun Shen, Guosheng Yin, Lequan Yu
Joint Distributional Learning via Cramer-Wold Distance
Seunghwan An, Jong-June Jeon
High-dimensional Bayesian Optimization with Group Testing
Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier, Luigi Nardi
${\tt MORALS}$: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space
Ewerton R. Vieira, Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Konstantin Mischaikow, Kostas E. Bekris