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 11
GTDD: Generative Test-Driven Development for AI Coding Agents with Adversarial Testing
Test-driven development gives AI coding agents executable requirements for implementing software.Simulation-Free Learning of Population Dynamics with Wasserstein Lagrangian Residuals
The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time.Targeted Active Learning for Preference-Based Treatment Effects on Multivariate Outcomes
Treatment efficacy is traditionally demonstrated on the basis of a single primary outcome.Co-Supervised Tree Synthesis for Interpretable Subgroup Identification and Honest Treatment Effect Inference in Randomized Trials
Identifying patient subgroups with heterogeneous treatment effects is central to precision medicine, yet existing approaches face a tension.The Price of Correlated Tests: How Strict Should a Model Release Gate Be?
Before a machine learning model ships, it often has to pass a suite of automated tests.Tolerance-Based Fairness Auditing: Violation Certification and Sensitivity Screening
As artificial intelligence is increasingly deployed, algorithmic unfairness has raised growing concerns and intensified demands for transparent fairness auditing.Gradient-Guided Density Peak Clustering
Density peak clustering (DPC) connects each observation to its nearest neighbor of higher density and identifies cluster centers as high-density observations with unusually large nearest neighbor…Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures
The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian location mixture maximizes the likelihood over the infinite-dimensional space of mixing distributions.Certified Alpha Capacity: Statistical Arbitrage When Learning Takes Time
Statistical validation takes time, and alpha can decay before the evidence justifies deployment.On skew-symmetric distributions and their use in Monte Carlo sampling algorithms: coordinate-free, Gibbs-style and manifold versions of the Barker proposal
Skew-symmetric probability distributions provide a principled mechanism for incorporating gradient information into Markov chain Monte Carlo algorithms.Generalized Engression Models
We consider estimating the conditional distribution of a multivariate outcome given covariates when its coordinates may be continuous, binary, categorical, ordinal or rankings, and are conditionally…Always-On Experimentation
Generative AI has dramatically accelerated the rate at which new treatments---from novel pharmaceuticals to online marketing campaigns---can be conceived and deployed.Optimal Allocation and Volume under Surface
This paper develops a framework for estimation and inference on the volumes of sets that are projections of critical function sets, focusing particularly on the convex body beneath the optimal…Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision
Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intractable statistical models and offers near…Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoods
We consider simulation-based Bayesian inference (SBI) for the parameters of models with intractable likelihoods but tractable forward simulation.BayesNDE: Bayesian Generative Modeling for Neural Density Estimation
Density estimation is a fundamental problem in statistics and machine learning.Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding
Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured.Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach
Classical variance-based Global Sensitivity Analysis (GSA) assumes that the input--output mechanism can be repeatedly evaluated under a designed sampling scheme, which is infeasible when only a…Heteroskedastic Canonical Polyadic Tensor Decomposition
When minimizing the squared-error loss, the popular CP decomposition can be interpreted as parameter inference in a Gaussian model with a low-rank mean tensor and constant variance across the tensor…Optimal detection of general moment changes: Simultaneous mean and covariance change detection and beyond
We study multiple change-point detection in multivariate time series whose distributions change in a piecewise constant manner.