Probability & Statistics · Mathematics & Statistics
Methodology
The newest papers in Methodology that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
353 papers, from 4 September 2026 to 5 November 2026: 353 arXiv preprints.
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Newest papers, page 3
Reinforcement Learning Under Runtime Confounding: Policy Learning and Off-Policy Evaluation
Reinforcement learning (RL) is increasingly used to train robust, adaptable systems across healthcare, e-commerce, and robotics.Conformal Annotation with Randomized Expert Review: Application to Online Cell-Type Labeling
Artificial intelligence (AI) models can accelerate cell-type annotation in digital pathology, but safely scaling their use requires determining, with error control, which model-generated labels can…Optimal Neural Estimation for High-Dimensional Nonparametric Regression under Multisource Covariate Shift
We study high-dimensional nonparametric regression under multisource covariate shift, where $\mathrm J$ sources of training data share a common regression function but differ in their covariate…Integrating multi-source local information for global undirected graph reconstruction via integer programming
Graphical models are widely used to represent conditional independence structures, but learning them is challenging when data are fragmented across sources that measure different subsets of…Metropolized backward sampling for conditional sequential Monte Carlo
Particle Gibbs samplers with backward sampling are widely used for sampling the smoothing distribution of state-space models, but, with few particles, the new trajectory often coincides with the…Pinpointing Pivot Points: The DECIDE Framework for Data Analysis Decision Importances
Human judgment calls made throughout the data science life cycle can change the conclusions drawn from data.Tabular foundation models are not a shortcut to calibrated clinical risk prediction: a pre-registered simulation against the true risk
Tabular foundation models are pretrained networks that predict risk from a table without fitting a model.Balanced State Subspaces for Mamba-2 State Compression
Mamba-2 compresses all context into a fixed-size recurrent state that dominates decoding memory and bandwidth.LLM knowledge for improved Out-of-Distribution Tabular Prediction - A Cross-Validated Regularisation Approach
Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer.Joint Distributional Copula Regression for Insurance Claim Frequency and Severity via Boosting
In this paper, we introduce a model-based boosting approach for fitting structured additive distributional copula regression models for bivariate responses, with particular emphasis on jointly…Cost-based variable selection for prediction models
Variable selection for clinical prediction models is commonly done on statistical grounds related to model fit.Agentic Distributional Regression: Conditional Density Estimation through Agent-Written Programs
Artificial intelligence offers statisticians a new paradigm for modelling and analysing complex datasets.Pro-Active Inference
Estimating a population mean is fundamental across scientific applications, but observing every outcome can be prohibitively expensive.Designing Customer Feedback for Subscription Boxes
Problem definition: Subscription box services often ask customers for ratings before choosing what to send.Conformal Novelty Detection via Data-enhanced Localization
Novelty detection is an important area of research in both statistics and machine learning.Recertifying decisions via e-closure: uniform improvements of conformal risk control and randomized e-BH
Statistical procedures often control expected loss at a prescribed level while leaving part of their risk budget unused.Directional Statistics on Circles and Spheres: A New Angle on Data
Two compass bearings near north can have an ordinary arithmetic mean pointing south: the difficulty is that angles live on a circle, not a straight line.Synthetic Data Augmentation for Functional Data: Wasserstein Risk Bounds and Diffusion-Based Conditional Generation
Functional data are costly to collect; samples of tens to hundreds of curves are common.Conformal Prediction with Stable Measurement Mechanisms
In applications such as air-quality monitoring, inexpensive sensor readings can help predict pollutant concentrations measured by a separate reference instrument.Inference for Causal Path-Specific Effects in Longitudinal Studies with Repeated Treatments and Multiple Mediators
In longitudinal studies, repeated treatments may be followed by different sequences of mediators and time-varying covariates.