Local geometry of high-dimensional mixture models: Effective spectral theory and dynamical transitions

Aukosh Jagannath, Gerard Ben Arous, Reza Gheissari et al.

Dual Induction CLT for High-dimensional m-dependent Data

Heejong Bong, Arun Kumar Kuchibhotla, Alessandro Rinaldo

Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition

Defeng Sun, Zhenting Luan, Haoning Wang et al.

Real-time prediction plays a vital role in various control systems, such as traffic congestion control and wireless channel resource allocation. In th...

Algorithms for ridge estimation with convergence guarantees

Wanli Qiao, Wolfgang Polonik

The extraction of filamentary structure from a point cloud is discussed. The filaments are modeled as ridge lines or higher dimensional ridges of an u...

Inferring Change Points in High-Dimensional Regression via Approximate Message Passing

Gabriel Arpino, Xiaoqi Liu, Julia Gontarek et al.

We consider the problem of localizing change points in a generalized linear model (GLM), a model that covers many widely studied problems in statistic...

Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions

Qian Lin, Haobo Zhang, Yicheng Li et al.

Motivated by studies of neural networks, particularly the neural tangent kernel theory, we investigate the large-dimensional behavior of kernel ridge ...

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data

Yang Ni, Hee Cheol Chung, Irina Gaynanova

Sequencing-based technologies provide an abundance of high-dimensional biological data sets with highly skewed and zero-inflated measurements. Despite...

Optimal subsampling for high-dimensional partially linear models via machine learning methods

Lei Wang, Heng Lian, Yujing Shao et al.

In this paper, we explore optimal subsampling strategies for estimating the parametric regression coefficients in partially linear models with unknown...

Decentralized Sparse Linear Regression via Gradient-Tracking

Ying Sun, Guang Cheng, Marie Maros et al.

We study sparse linear regression over a network of agents, modeled as an undirected graph without a center node. The estimation of the $s$-sparse ...

A non-asymptotic distributional theory of approximate message passing for sparse and robust regression

Gen Li, Yuting Wei

Reviving pseudo-inverses: Asymptotic properties of large dimensional Moore-Penrose and Ridge-type inverses with applications

Taras Bodnar, Nestor Parolya

Generalized Linear Spectral Statistics of High-dimensional Sample Covariance Matrices and Its Applications

Yanlin Hu, Qing Yang, Xiao Han

The out-of sample prediction error of the $\sqrt{\text{LASSO}}$ and related estimators

José Luis Montiel Olea, Cynthia Rush, Amilcar Velez et al.

Conjugate gradient methods for high-dimensional GLMMs

Andrea Pandolfi, Omiros Papaspiliopoulos, Giacomo Zanella

Finite- and large-sample inference for model and coefficients in high-dimensional linear regression with repro samples

Linjun Zhang, Peng Wang, Minge Xie

Spectral change point estimation for high-dimensional time series by sparse tensor decompositionGet access

Xinyu ZhangandKung-Sik Chan

Trace Test for High-Dimensional Cointegration

Alexei Onatski, Chen Wang

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Cong Fang, Weijie J. Su, Jiancong Xiao et al.

Simplex Constrained Sparse Optimization via Tail Screening

Xueqin Wang, Peng Chen, Jin Zhu et al.

We consider the probabilistic simplex-constrained sparse recovery problem. The commonly used Lasso-type penalty for promoting sparsity is ineffective ...

Finite Expression Method for Solving High-Dimensional Partial Differential Equations

Senwei Liang, Haizhao Yang

Designing efficient and accurate numerical solvers for high-dimensional partial differential equations (PDEs) remains a challenging and important topi...

Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions

Penghe Zhang, Naihua Xiu, Hou-Duo Qi

The hard-margin loss function has been at the core of the support vector machine research from the very beginning due to its generalization capability...

Word-Level Maximum Mean Discrepancy Regularization for Word Embedding

Youqian Gao, Ben Dai

Communication-Efficient and Distributed-Oracle Estimation for High-Dimensional Quantile Regression

Xuming He, Songshan Yang, Yifan Gu et al.

Pretraining and the lassoGet access

Erin Craigand others

Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift

Kaizheng Wang

Kernel Spectral Joint Embeddings for High-Dimensional Noisy Datasets using Duo-Landmark Integral Operators

Xiucai Ding, Rong Ma

Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition

Andrea Montanari, Yuchen Wu

High-Dimensional Hilbert-Schmidt Linear Regression with Hilbert Manifold Variables

Changwon Choi, Byeong U. Park

A Geometrical Analysis of Kernel Ridge Regression and its Applications

Zong Shang, Guillaume Lecué, Georgios Gavrilopoulos

Sparse PCA: A New Scalable Estimator Based on Integer Programming

Kayhan Behdin, Rahul Mazumder

The High-Dimensional Asymptotics of Principal Component Regression

Alden Green, Elad Romanov

Advances in Bayesian Model Selection Consistency for High-Dimensional Generalized Linear Models

Jeyong Lee, Minwoo Chae, Ryan Martin

High-Dimensional Statistical Inference for Linkage Disequilibrium Score Regression and Its Cross-Ancestry Extensions

Fei Xue, Bingxin Zhao

The Functional Graphical Lasso

Kartik Govind Waghmare, Tomas Masak, Victor Michael Panaretos

Debiased Regression Adjustment in Completely Randomized Experiments with Moderately High-Dimensional Covariates

Xin Lu, Fan Yang, Yuhao Wang

Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers

Fan Yang, Hongyang R. Zhang, Sen Wu et al.

The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following q...

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

Shao-Bo Lin, Xiaotong Liu, Di Wang et al.

Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for ...

Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors

Kean Ming Tan, Wen-Xin Zhou, Gaoyu Wu et al.

Expected shortfall (ES) is widely used for characterizing the tail of a distribution across various fields, particularly in financial risk management....

Outlier Robust and Sparse Estimation of Linear Regression Coefficients

Takeyuki Sasai, Hironori Fujisawa

We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial...

High-Dimensional L2-Boosting: Rate of Convergence

Ye Luo, Martin Spindler, Jannis Kueck

Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of L2-Boosting in a high-dimensio...

Rank-one Convexification for Sparse Regression

Alper Atamturk, Andres Gomez

Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact m...

Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

Fangzheng Xie, Yanxun Xu, Dapeng Yao

We study the sparse high-dimensional Gaussian mixture model when the number of clusters is allowed to grow with the sample size. A minimax lower bound...

From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

Xinghao Qiao, Dong Li, Shaojun Guo et al.

Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most...

DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data

Jiayi Tong, Jie Hu, George Hripcsak et al.

High-dimensional healthcare data, such as electronic health records (EHR) data and claims data, present two primary challenges due to the large number...

Identifying the Structure of High-Dimensional Time Series via Eigen-Analysis

Bo Zhang, Jiti Gao, Guangming Pan et al.

SOFARI: High-Dimensional Manifold-Based Inference

Zemin Zheng, Xin Zhou, Yingying Fan et al.

Dimension estimation in a spiked covariance model using high-dimensional data augmentation

U Radojičić, J Virta

Abstract

Higher Order Accurate Symmetric Bootstrap Confidence Intervals in High Dimensional Penalized Regression

Debraj Das, Arindam Chatterjee, S. N. Lahiri

Simulating diffusion bridges with score matching

J Heng, others

Abstract

High-dimensional covariance regression with application to co-expression QTL detection

Rakheon Kim, Jingfei Zhang

Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions

Anders B. Kock, Rasmus S. Pedersen, Jesper R.-V. Sørensen

Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling

Xiaotong Lin, others

Detection and inference of changes in high-dimensional linear regression with nonsparse structures

Haeran Cho, others

Sparse Bayesian Multidimensional Item Response Theory

Jiguang Li, Robert Gibbons, Veronika Ročková

An optimal design framework for lasso sign recovery

Jonathan W Stallrich, others

Communication-Efficient Distributed Sparse Learning with Oracle Property and Geometric Convergence

Weidong Liu, Jiyuan Tu, Xiaojun Mao

Statistical Inference for High-Dimensional Spectral Density Matrix

Jinyuan Chang, Xiaofeng Shao, Qing Jiang et al.

Frequency Domain Statistical Inference for High-Dimensional Time Series

Jonas Krampe, Efstathios Paparoditis

Kernel Meets Sieve: Transformed Hazards Models with Sparse Longitudinal Covariates

Dayu Sun, Zhuowei Sun, Xingqiu Zhao et al.

Class-Specific Joint Feature Screening in Ultrahigh-Dimensional Mixture Regression

Kaili Jing, Abbas Khalili, Chen Xu

Degree-Heterogeneous Latent Class Analysis for High-Dimensional Discrete Data

Zhongyuan Lyu, Ling Chen, Yuqi Gu

Statistical Inference for High-Dimensional Convoluted Rank Regression

Liping Zhu, Leheng Cai, Xu Guo et al.

High-Dimensional Variable Clustering based on Maxima of a Weakly Dependent Random Process

Alexis Boulin, Elena Di Bernardino, Thomas Laloë et al.

Strong oracle guarantees for partial penalized tests of high-dimensional generalized linear models

Tate Jacobson

High-Dimensional Expected Shortfall Regression

Xuming He, Kean Ming Tan, Wen-Xin Zhou et al.

Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling

Jinyuan Chang, others

High-Dimensional Knockoffs Inference for Time Series Data

Yingying Fan, Jinchi Lv, Chien-Ming Chi et al.

An Adaptive Adjustment to theR2Statistic in High-Dimensional Elliptical Models

Shizhe Hong, Weiming Li, Qiang Liu et al.

High-dimensional Factor Analysis for Network-linked data

Jinming Li, others

Abstract

Adaptive Testing for High-Dimensional Data

Xiaofeng Shao, Yangfan Zhang, Runmin Wang

Comparison of Longitudinal Trajectories Using a High-Dimensional Partial Linear Semiparametric Mixed-Effects Model

Sami Leon, Tong Tong Wu

Deconvolution Density Estimation with Penalized MLE

Yun Cai, Hong Gu, Toby Kenney

Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity

Edgar Dobriban, Xinmeng Huang, Kan Xu et al.

On the Modeling and Prediction of High-Dimensional Functional Time Series

Qiwei Yao, Jinyuan Chang, Xinghao Qiao et al.