Papers to Appear in Subsequent Issues

When papers are accepted for publication, they will appear below. Any changes that are made during the production process will only appear in the final version. Papers listed here are not updated during the production process and are removed once an issue is published.

 

Optimal Integrative Estimation for Distributed Precision Matrices with Heterogeneity Adjustment Yinrui Sun and Yin Xia
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation Yanhao Jin, Krishnakumar Balasubramanian and Debashis Paul
Inferring diffusivity from killed diffusion Richard Nickl and Fanny Seizilles
No-Regret Generative Modeling via Parabolic Monge-Ampère PDE Nabarun Deb and Tengyuan Liang
Fast convergence rates for estimating the stationary density in SDEs driven by a fractional Brownian motion with semi-contractive drift Chiara Amorino, Eulalia Nualart, Fabien Panloup and Julian Sieber
Gaussian and non-Gaussian Universality of Data Augmentation Kevin Han Huang, Peter Orbanz and Morgane Austern
Data assimilation with the $2D$ Navier-Stokes equations: Optimal Gaussian asymptotics for the posterior measure Dimitri Konen and Richard Nickl
Active Subsampling for Measurement-Constrained M-Estimation of Individualized Thresholds with High-Dimensional Data Jingyi Duan, Lehao Fu and Yang Ning
Granulometric Smoothing on Manifolds Diego Bolón, Rosa María Crujeiras and Alberto Rodríguez-Casal
Consistent Infill Estimability of the Regression Slope Between Gaussian Random Fields Under Spatial Confounding Abhirup Datta and Michael L. Stein
Consistent Bayesian Spatial Domain Partitioning Using Predictive Spanning Tree Methods Kun Huang and Huiyan Sang
Local minima of the empirical risk in high dimension: General theorems and convex examples Kiana Asgari, Andrea Montanari and Basil Saeed
Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional Sean McGrath and Rajarshi Mukherjee
Spectral Asymptotics of Neural Network Jacobians: Convergency, Universality, and Phase Transition Huiqin Li, Guangming Pan and Yanqing Yin
SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals Jianqing Fan, Yingying Fan, Jinchi Lv and Fan Yang
Alignment and matching tests for high-dimensional tensor signals via tensor contraction Ruihan Liu, Zhenggang Wang and Jianfeng Yao
Power properties of the two-sample test based on the nearest neighbors graph Rahul Raphael Kanekar
On importance sampling and independent Metropolis-Hastings with an unbounded weight function Pierre Etienne Jacob
Fast Wasserstein rates for estimating probability distributions of probabilistic graphical models Daniel Bartl and Stephan Eckstein
Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Haoran Li
Smooth Flow Matching Jianbin Tan and Anru Zhang
Minmax Trend Filtering: Generalizations of Total Variation Denoising via a Local Minmax/Maxmin Formula Sabyasachi Chatterjee
Principled Analysis of Crossover Designs: Causal Effects, Efficient Estimation, and Robust Inference Peng Ding and Zhichao Jiang
Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise Xiucai Ding, Chao Shen and Hau-Tieng Wu
Revisiting mean estimation over lp balls: Is the MLE optimal? Liviu Aolaritei, Michael Jordan, Reese Pathak and Annie Ulichney
Functional linear regression from sparse to dense designs: a pooling-ridge method and minimax optimality Shunxing Yan and Fang Yao