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

March 5, 2024

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
Masahiro Kato
High Dimensional Causal Effect Conditional Average Treatment Effect Statistical Inference LASSO Regression

March 4, 2024

From Zero to Hero: How local curvature at artless initial conditions leads away from bad minima
Tony Bonnaire, Giulio Biroli, Chiara Cammarota
Gradient Descent High Dimensional Hessian Matrix Better Zero Dynamic Optimization Zero to HeRo Gradient Descent Dynamic Initial Condition

March 2, 2024

February 26, 2024

February 21, 2024

Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
General Analysis High Dimensional Statistical Model Generalized Linear Model Error Estimation Resampling Method

February 17, 2024

Evaluating the Stability of Deep Learning Latent Feature Spaces
Ademide O. Mabadeje, Michael J. Pyrcz
Latent Space High Dimensional Core Stability Latent Space Dynamic

February 14, 2024

MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection
Xubin Wang, Haojiong Shangguan, Fengyi Huang, Shangrui Wu, Weijia Jia
High Dimensional Feature Selection Data Dimensionality Evolutionary Multitasking

February 13, 2024

February 12, 2024

Model Collapse Demystified: The Case of Regression
Elvis Dohmatob, Yunzhen Feng, Julia Kempe
High Dimensional Case Relevance Novel Regression Image Generation Model Adaptive Regularization Model Collapse Crossover Probability

February 10, 2024

DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
Parisa Salmanian, Angelos Chatzimparmpas, Ali Can Karaca, Rafael M. Martins
High Dimensional Dimensionality Reduction Feature Relevance Explainable Boosting Machine Visual Clustering

February 7, 2024

Asymptotics of feature learning in two-layer networks after one gradient-step
Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro
High Dimensional Feature Learning Two Layer Neural Network Asymptotic Behavior Two Layer Network Kernel Regime Gradient Step Gaussian Universality

February 6, 2024

Operator SVD with Neural Networks via Nested Low-Rank Approximation
J. Jon Ryu, Xiangxiang Xu, H. S. Melihcan Erol, Yuheng Bu, Lizhong Zheng, Gregory W. Wornell
Neural Network High Dimensional Average Approximation Low Rank Approximation Singular Value Decomposition Nested Entity Eigenfunction Decomposition Truncated Singular Value Decomposition

February 5, 2024

February 3, 2024

Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, Titouan Vayer
High Dimensional Unsupervised Learning Dimensionality Reduction Gromov Wasserstein Prototype Quality Distributional Reduction

High Dimensional

Papers

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects

From Zero to Hero: How local curvature at artless initial conditions leads away from bad minima

High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media

$\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data

Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent

Penalized Generative Variable Selection

Stable Training of Normalizing Flows for High-dimensional Variational Inference

Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression

Evaluating the Stability of Deep Learning Latent Feature Spaces

MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection

Space-Time Bridge-Diffusion

Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems

Model Collapse Demystified: The Case of Regression

DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine

Asymptotics of feature learning in two-layer networks after one gradient-step

Operator SVD with Neural Networks via Nested Low-Rank Approximation

nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy

Standard Gaussian Process Can Be Excellent for High-Dimensional Bayesian Optimization

Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein