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
SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification
Alan John Varghese, Zhen Zhang, George Em Karniadakis
High-Dimensional Sparse Data Low-rank Representation via Accelerated Asynchronous Parallel Stochastic Gradient Descent
Qicong Hu, Hao Wu
An Adaptive Latent Factorization of Tensors Model for Embedding Dynamic Communication Network
Xin Liao, Qicong Hu, Peng Tang
Safe Bayesian Optimization for High-Dimensional Control Systems via Additive Gaussian Processes
Hongxuan Wang, Xiaocong Li, Adrish Bhaumik, Prahlad Vadakkepat
Risk and cross validation in ridge regression with correlated samples
Alexander Atanasov, Jacob A. Zavatone-Veth, Cengiz Pehlevan
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
Xin Zhou, Weiqing Wang, Wray Buntine, Shilin Qu, Abishek Sriramulu, Weicong Tan, Christoph Bergmeir
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions
Luca Reichmann, David Hägele, Daniel Weiskopf
Hierarchical Quantum Control Gates for Functional MRI Understanding
Xuan-Bac Nguyen, Hoang-Quan Nguyen, Hugh Churchill, Samee U. Khan, Khoa Luu
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN
Chang Yu, Yixin Jin, Qianwen Xing, Ye Zhang, Shaobo Guo, Shuchen Meng