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
Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems
Jake Welde, Nishanth Rao, Pratik Kunapuli, Dinesh Jayaraman, Vijay Kumar
MoDex: Planning High-Dimensional Dexterous Control via Learning Neural Hand Models
Tong Wu, Shoujie Li, Chuqiao Lyu, Kit-Wa Sou, Wang-Sing Chan, Wenbo Ding
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 Complex Control Systems via Additive Gaussian Processes
Hongxuan Wang, Xiaocong Li, Lihao Zheng, Adrish Bhaumik, Prahlad Vadakkepat