Papers
Found 64 papers
Sorted by: Newest FirstSolving the Poisson Equation Using Coupled Markov Chains
Pierre Etienne Jacob, Randal Douc, Anthony Lee et al.
Average Partial Effect Estimation Using Double Machine Learning
Harvey Klyne, Rajen Shah
Fundamental Limits of Community Detection From Multi-View Data: Multi-Layer, Dynamic and Partially Labeled Block Models
Xiaodong Yang, Buyu Lin, Subhabrata Sen
Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data
Tianyu Zhang, Jing Lei
Poisson Empirical Bayes Estimation: When Doesg-Modeling Beatf-Modeling in Theory (And in Practice)?
Yandi Shen, Yihong Wu
High-Dimensional Hilbert-Schmidt Linear Regression with Hilbert Manifold Variables
Changwon Choi, Byeong U. Park
Optimal Sequencing Depth for Single-Cell RNA-Sequencing in Wasserstein Space
Jakwang Kim, Sharvaj Kubal, Geoffrey Schiebinger
A Two-Way Heterogeneity Model for Dynamic Networks
Binyan Jiang, Chenlei Leng, Ting Yan et al.
A Geometrical Analysis of Kernel Ridge Regression and its Applications
Zong Shang, Guillaume Lecué, Georgios Gavrilopoulos
Kurtosis-Based Projection Pursuit for Matrix-Valued Data
Una Radojicic, Klaus Nordhausen, Joni Virta
A Flexible Defense Against the Winner’s Curse
Tijana Zrnic, William Fithian
Rank Tests for PCA Under Weak Identifiability
Davy Paindaveine, Laura Peralvo Maroto, Thomas Verdebout
Sparse PCA: A New Scalable Estimator Based on Integer Programming
Kayhan Behdin, Rahul Mazumder
Semi-Supervised U-Statistics
Ilmun Kim, Larry Wasserman, Sivaraman Balakrishnan et al.
Scalable Inference in Functional Linear Regression with Streaming Data
Jinhan Xie, Enze Shi, Peijun Sang et al.
The Empirical Copula Process in High Dimensions: Stute’s Representation and Applications
Axel Bücher, Cambyse Pakzad
Causal Effect Estimation Under Network Interference with Mean-Field Methods
Sohom Bhattacharya, Subhabrata Sen
Clustering risk in Non-parametric Hidden Markov and I.I.D. Models
Elisabeth Gassiat, Ibrahim Kaddouri, Zacharie Naulet
Efficiently Matching Random Inhomogeneous Graphs via Degree Profiles
Jian Ding, Yumou Fei, Yuanzheng Wang
Improving Knockoffs with Conditional Calibration
Yixiang Luo, William Fithian, Lihua Lei
Spectral Density Estimation of Function-Valued Spatial Processes
Rafail Kartsioukas, Stilian Stoev, Tailen Hsing
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu, Cong Fang, Peter Bühlmann et al.
Tests of Missing Completely at Random Based on Sample Covariance Matrices
Alberto Bordino, Thomas Benjamin Berrett
Near Optimal Sample Complexity for Matrix and Tensor Normal Models via Geodesic Convexity
Rafael Mendes de Oliveira, William Cole Franks, Akshay Ramachandran et al.
Yurinskii’s Coupling for Martingales
Matias Damian Cattaneo, Ricardo Pereira Masini, William George Underwood
Improved Learning Theory for Kernel Distribution Regression with Two-Stage Sampling
François Bachoc, Louis Béthune, Alberto González-Sanz et al.
Trimmed Sample Means for Robust Uniform Mean Estimation and Regression
Roberto Imbuzeiro Moraes Felinto de Oliveira, Lucas Resende
Pseudo-Likelihood-Based M-Estimation of Random Graphs with Dependent Edges and Parameter Vectors of Increasing Dimension
Jonathan Roy Stewart, Michael Schweinberger
Robust Transfer Learning with Unreliable Source Data
Jianqing Fan, Cheng Gao, Jason Matthew Klusowski
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
Weidong Liu, Jiyuan Tu, Yichen Zhang et al.
The High-Dimensional Asymptotics of Principal Component Regression
Alden Green, Elad Romanov
Theory of Functional Principal Component Analysis for Discretely Observed Data
Hang Zhou, Dongyi Wei, Fang Yao
A Unified Analysis of Likelihood-based Estimators in the Plackett–Luce Model
Ruijian Han, Yiming Xu
Symmetry: A General Structure in Nonparametric Regression
Louis Goldwater Christie, John A. D. Aston
Advances in Bayesian Model Selection Consistency for High-Dimensional Generalized Linear Models
Jeyong Lee, Minwoo Chae, Ryan Martin
Estimation and Inference in Distributional Reinforcement Learning
Liangyu Zhang, Yang Peng, Jiadong Liang et al.
Online Statistical Inference in Decision Making with Matrix Context
Qiyu Han, Will Wei Sun, Yichen Zhang
Structured Matrix Learning under Arbitrary Entrywise Dependence and Estimation of Markov Transition Kernel
Jinhang Chai, Jianqing Fan
Optimal and Exact Recovery on the General Non-Uniform Hypergraph Stochastic Block Model
Ioana Dumitriu, Hai-Xiao Wang
High-Dimensional Statistical Inference for Linkage Disequilibrium Score Regression and Its Cross-Ancestry Extensions
Fei Xue, Bingxin Zhao
Deep Horseshoe Gaussian Processes
Ismaël Castillo, Thibault Christophe Randrianarisoa
The Functional Graphical Lasso
Kartik Govind Waghmare, Tomas Masak, Victor Michael Panaretos
Higher-Order Entrywise Eigenvectors Analysis of Low-Rank Random Matrices: Bias Correction, Edgeworth Expansion, and Bootstrap
Fangzheng Xie, Yichi Zhang
Counterfactual Inference in Sequential Experiments
Raaz Dwivedi, Katherine Tian, Sabina Tomkins et al.
Optimal Vintage Factor Analysis with Deflation Varimax
Xin Bing, Xin He, Dian Jin et al.
Low-Degree Hardness of Detection for Correlated Erdős-Rényi Graphs
Jian Ding, Hang Du, Zhangsong Li
Spectral Gap Bounds for Reversible Hybrid Gibbs Chains
Qian Qin, Nianqiao Ju, Guanyang Wang
Fixed and Random Covariance Regression Analyses
Tao Zou, Wei Lan, Runze Li et al.
Debiased Regression Adjustment in Completely Randomized Experiments with Moderately High-Dimensional Covariates
Xin Lu, Fan Yang, Yuhao Wang
Reinforcement Learning for Individual Optimal Policy From Heterogeneous Data
Rui Miao, Babak Shahbaba, Annie Qu
Policy Learning “Without” Overlap: Pessimism and Generalized Empirical Bernstein’s Inequality
Ying Jin, Zhimei Ren, Zhuoran Yang et al.
Algorithmic Stability Implies Training-Conditional Coverage for Distribution-Free Prediction Methods
Ruiting Liang, Rina Foygel Barber
Semiparametric Modeling and Analysis for Longitudinal Network Data
Yinqiu He, Jiajin Sun, Yuang Tian et al.
On the Structural Dimension of Sliced Inverse Regression
Dongming Huang, Songtao Tian, Qian Lin
Erratum: Quantile Processes and Their Applications in Finite Populations
Anurag Dey, Probal Chaudhuri
Dualizing Le Cam’s Method for Functional Estimation, with Applications to Estimating the Unseens
Yury Polyanskiy, Yihong Wu
Asymptotically-Exact Selective Inference for Quantile Regression
Yumeng Wang, Snigdha Panigrahi, Xuming He
Near-Optimal Inference in Adaptive Linear Regression
Koulik Khamaru, Yash Deshpande, Tor Lattimore et al.
A Common-Cause Principle for Eliminating Selection Bias in Causal Estimands Through Covariate Adjustment
Maya Mathur, Ilya Shpitser, Tyler VanderWeele