Probability & Statistics · Mathematics & Statistics
Statistics Theory
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321 papers, from 31 August 2026 to 4 November 2026: 287 arXiv preprints and 34 OpenAlex records.
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Newest papers, page 7
When do explicit penalties improve flow matching?
Flow matching can learn structure directly from data, raising the question of when additional constraints improve generation.Adaptive Robust Confidence Sets in High Dimensions
We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown.Inference on the Value of Policy Improvement in Adaptive Experiments
In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data.Separating Statistical Inference from Experimental Runtime: An Architecture for Sequential Scientific Analysis
Sequential statistical procedures are increasingly embedded in scientific systems in which observations arrive incrementally from heterogeneous sources.Small area learning
Small area estimation (SAE) traditionally borrows strength across domains through a prespecified working model.Prior-Informed Generalizable Low-Rank Representation Learning via Regularized Adversarial Reweighting
Modern learning problems increasingly leverage data from heterogeneous sources to improve robustness and generalizability under distribution shift, often with high-dimensional parameters admitting…Copula Active Subspaces II: Error Decomposition, A Posteriori Estimation, and Sharpness of the Bounds
The Copula Active Subspaces (CAS) method of Part I estimates a non-Gaussian observation-noise density $π_{\boldsymbol{n}}$ from residual samples.DisCo Algebra: Why Disintegration and Combination Are Not Inverses
We present DisCo algebra---a compositional framework for joint probability built from a symmetric cooperad (disintegration) and a symmetric operad (combination) on the bare family…Energy-Guided Learning for Heat Equation with Noisy Source: Optimal Estimation and Generalization Bounds
Data and the governing partial differential equations (PDEs) of continuous-time dynamical systems provide rich structure for statistical learning.Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate
Compression schemes provide a general framework for deriving data-dependent risk bounds for learning algorithms.Beyond Discrepancy: Oracle Drift in Nonstationary Weighted Risk Minimization
We analyze distributional drift in nonstationary weighted risk minimization by decomposing the excess risk into estimation, drift, and approximation components.Decision-Sufficient Posterior Approximation
We investigate the consequences of requiring a posterior approximation to preserve a specified downstream decision problem.Sequential Conditional Independence Testing with Machine Learning Models
Conditional independence testing is a ubiquitous problem in scientific discovery.Optimal random quantisers for spherically symmetric distributions
Zador's celebrated theorem is a cornerstone of optimal quantisation: it establishes both the weak limit of the empirical distribution of an optimal $n$-point quantiser in $R^d$ and the decay rate of…Conditional Kernel Stein Discrepancy
Kernel Stein discrepancies (KSDs) provide a versatile tool for comparing distributions.Learning structured linear dynamical systems from missing observations
We consider the problem of learning structured linear dynamical systems over convex sets $\mathcal{K}$, where only a small subset of the observations are available at each time point.RobustLDS: Learning linear dynamical systems under adversarial corruptions
We consider the problem of learning linear dynamical systems under adversarial contamination from a single trajectory of length $T$.Efficient quadratic entropy with distance sketches
We detail scalable methods for approximating the quadratic entropy $p^T d p$ for arbitrary distributions $p$ and common distances $d$ of negative type.Testing Algebraic Complete Intersections
Given independent and identically distributed samples samples from a probability distribution in a potentially high-dimensional real space, we study the problem of testing whether the distribution…Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure
Masked diffusion models (MDMs) generate discrete data by iteratively unmasking multiple tokens in parallel using a learned mask predictor.