Probability & Statistics · Mathematics & Statistics
Statistics Theory
The newest papers in Statistics Theory that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
321 papers, from 31 August 2026 to 4 November 2026: 287 arXiv preprints and 34 OpenAlex records.
Get the few papers worth your time in Statistics Theory every morning.
Follow Statistics Theory and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 13
The Critical Line Algorithm and the Constrained LASSO: One Curve, Two Literatures
Many statistical procedures compute an entire solution path rather than a single estimator.Doubly robust target inference for generalized linear regression with completely missing covariates
Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses.Adversarially Robust PAC Learning with Optimal VC Rates
We study the problem of \emph{adversarially robust} PAC learning.Improved polynomial-time algorithms for detecting and recovering planted $Θ(\sqrt{n})$-cliques
In the planted clique problem, one observes either an Erdős--Rényi graph on $n$ vertices or such a graph with a clique added to $k = k(n)$ vertices, and seeks to detect or recover the clique.Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR).PICPIs: Prediction-Interval-Conditional Prediction Intervals
A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets.A Kernel Approach to Multivariate Generalized Gammas Convolutions
We propose a kernel-based estimation framework for Multivariate Generalized Gamma convolutions (MGGC), a class of probability distributions on R d + .Belted Engression: Sufficient Dimension Reduction for Generative Distributional Regression
Modern conditional generative models face significant challenges when learning complex covariate dependencies.Toscani-Fourier Distance on Probability Measures: Wasserstein Control, Topological Equivalence on Model Classes, and Duality
Comparing probability measures in machine learning trades transport geometry against computational cost: Wasserstein distances encode the geometry of $\mathbb R^d$ but require solving a transport…Schedule optimization for tau-leaping in masked discrete diffusion
Masked discrete diffusion models are commonly accelerated using the so-called tau-leaping discretization method, which reveals several coordinates in parallel at each sampling step.Locally Private Inference for Riemannian Stochastic Optimization
We develop inference for manifold-valued population minimizers when each observation belongs to a different participant and only locally private messages reach the analyst.Next-token functional estimation
Suppose we observe the first $n$ points of a sequence of random variables having length $n+1$, and wish to estimate a functional of the unobserved final point and the empirical measure of the $n$…Sharp spectral norm concentration of sparse random tensors
We prove a sharp concentration inequality for the spectral norm of sparse random tensors with independent Bernoulli entries.Parallelism, critical windows, and separations among diffusion language models
A popular selling point of diffusion large language models (dLLMs) is their capacity for parallelism: the ability to generate sequences of text far more efficiently than autoregressive models, which…Principal component error in high-dimensional factor models
In a statistical factor model, principal components (or eigenvectors) of a sample covariance matrix serve as estimates of {\it principal directions}, the true drivers of co-movement of a collection…Robust Multi-Task Learning for Principal Component Analysis
Principal component analysis (PCA) is a fundamental tool for learning low-dimensional structure from high-dimensional data.Instance-Optimal Adaptive Location Estimation via Multiscale Mid-Summaries
Location estimation exhibits markedly different finite-sample behavior across noise distributions: regular families typically yield root-\(n\) rates, whereas compactly supported laws may admit…Aggregated Posterior Predictive Checks for Generative Modeling
Latent variable generative models are commonly fit using simple priors over latent variables, but draws from these priors often fail to produce realistic data.A Smoothed Discrepancy Principle for Random Feature Methods and Neural Networks
We study data-driven early stopping for spectral regularisation methods in the classical non-parametric regression setting.Triply-Scalable Equivariant Gaussian Process Modeling
Gaussian processes (GPs) provide principled probabilistic predictions while encoding prior knowledge, including equivariances.