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
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing
Kevin H. Huang, Xing Liu, Andrew B. Duncan, Axel Gandy
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Elisabeth Ailer, Jason Hartford, Niki Kilbertus
Orders-of-coupling representation with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization
Sergei Manzhos, Manabu Ihara
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions
Gavin Brown, Samuel B. Hopkins, Adam Smith
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning
Alec J. Linot, Kevin Zeng, Michael D. Graham
Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling
Suha Tuna, Cagri Gulec, Emrah Yucesan, Ayse Cirakoglu, Yelda Tarkan Arguden
Learning the Dynamics of Sparsely Observed Interacting Systems
Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux