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 4
Faithful Federated Tabular Data Synthesis
Synthetic data is an important data source in the AI era.Causal Inference in Metric Spaces via Distance Profiles
Modern interventions increasingly produce object-valued outcomes, such as distributions, covariance matrices, networks, and trees.Random Jump Forests
Random jump forests are introduced as a nonparametric tool for estimating conditional cumulative transition rates and occupation probabilities in multi-state models.Mixed Engression: Distributional Regression for Multivariate Mixed-Type Responses
Estimating the conditional distribution of multivariate responses with continuous and binary components requires capturing their dependence, respecting their different supports, and accommodating…Not All Mislabels Are Equal: Efficient Task-Aware Auditing with Confidence Sequences
We study efficient, adaptive auditing of categorical labels in a fixed dataset, motivated by biodiversity-credit programs in which expert verification of species records is costly and downstream…The kernel mean embedding of a random probability distribution and its principal components
Given a random probability distribution \(F\), whose realizations are defined on \(\mathbb R^d\), and a kernel \(k\), our aim is to analyze the properties of the kernel mean embedding, \(μ_F\), of…Lifetime-valid decisions by continually evolving AI analysts
AI agents increasingly act as analysts whose decision can often be cast as hypothesis tests.Prediction Uncertainty Controlled Computer Model Calibration
Computer-model calibration is usually undertaken in service of prediction, yet neither Bayesian Kennedy-O'Hagan (KOH) calibration nor amortized simulation-based inference is built to control the…Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains.Reliable AI-Assisted Decisions via Selective Auditing
We consider online monitoring of an AI system that produces a sequence of decisions.Operationalising Weaker Faithfulness: Recovery, Ambiguity and Computational Limits
Variables can predict an outcome jointly while each is individually independent of it, causing structure learners to discard informative links.Bayesian Propensity Score Predictive Inference for Extending Trial Findings to Target Populations
The CODIACS Vanguard randomized trial compared a depression care intervention with usual care for patients with persistent depressive symptoms following acute coronary syndrome.Lifting PCA in the Wasserstein Space
Principal Component Analysis (PCA) is a fundamental tool for statistical dimension reduction, yet no canonical extension to distribution-valued data has emerged within the framework of optimal…Mastering Model Fit: Applications of the InterModel Vigorish for Interpretable Machine Learning
Accuracy alone gives little information about which features or model components improve probabilistic fit, which outcomes benefit, or how fit changes during training.When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
Learned representations of high-dimensional multimodal health records are increasingly used to adjust for confounding, yet they are usually trained to reconstruct the data rather than preserve the…A Bayesian machine learning approach to direct and spillover effects in cluster-randomized trials with noncompliance
We develop a Bayesian machine learning approach for estimating direct and spillover effects in cluster-randomized trials with treatment noncompliance.On the Accuracy of Approximate Leave-One-Out with Blockwise Nonsmooth Regularization in High-dimensions
Approximate leave-one-out (ALO) refers to methods that provide computationally efficient and statistically accurate approximations to leave-one-out risk estimation.Active Pilot Sampling for Multi-fidelity Estimation in Stochastic Optimization
Stochastic optimization with high-fidelity nonlinear models is often computationally prohibitive because each objective evaluation requires sampling-based uncertainty quantification inside an outer…Divide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process Models
Spatial and spatiotemporal point process models are widely used to analyze event data across many scientific disciplines, but likelihood-based Bayesian inference is often computationally prohibitive…BARRR: Bidimensional Augmented Reduced Rank Regression for Multi-View Multi-Cohort Data
Bidimensional data integration across multiple cohorts (i.e., sample groups) and views (i.e., sources or modalities) is crucial in biomedical research to derive holistic insights from heterogeneous…