Probability & Statistics · Mathematics & Statistics
Computation
The newest papers in Computation that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
95 papers, from 4 September 2026 to 3 November 2026: 95 arXiv preprints.
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Newest papers, page 5
Repulsive normalizing flow mixtures for adaptive importance sampling: reliability analysis of complex systems
Accurate rare-event estimation can be computationally expensive.Robust Dual-Regularized Variable Selection under Outlier Contamination
Real data often contain unusual observations that can exert disproportionate effects on variable selection, especially in complex predictor settings.Scentree: a framework for generating scenario trees for multistage stochastic programming
We present scentree, an open-source Python package for constructing a scenario fan and a scenario tree for multistage stochastic programming from historical data.Neural composite likelihood estimation: simulation based inference for time series
Simulation based inference (SBI) circumvents the challenge of intractable likelihoods by using a simulator that generates data given parameter values.A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression
Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments.Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs
Accurate optimization of a supervised spectral objective need not produce an accurate population subspace or a better predictive representation.Triply-Scalable Equivariant Gaussian Process Modeling
Gaussian processes (GPs) provide principled probabilistic predictions while encoding prior knowledge, including equivariances.Quenched Ensemble Sampling
Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling algorithms stall.Copula Adapted Directed Acyclic Graph for Cluster Representation of Biomedical Data
Diagnostic errors and mislabeling are common in biomedicine, which compromise the reliability of predictive models and data-driven outcomes.Adaptive Conformal Redistribution for Inter-class Transitional Uncertainty in Medical Image Classification
Medical image classification is frequently complicated by transitional categories whose feature distributions overlap those of adjacent classes, producing ambiguous decision boundaries.The Elliptically Optimal Confidence Interval: A Bivariate Extension of Wilson's Score Method
Constructing a confidence interval for the difference between two independent binomial proportions involves a nuisance direction that is not identified by the estimand.Optimal Slice-Adaptive Tuning of Hybrid Slice Sampling
Slice sampling is a Markov chain Monte Carlo algorithm that draws its next state uniformly from a "slice"---a super-level set of the target density function---at each iteration, thereby providing…Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using only examples…Thermodynamic Cyclic Processes with Markov Samplers in Bayesian Inference
The concept of Markov chain Monte Carlo (MCMC) cycles, an analogy to cyclic processes in heat engines, is presented in order to examine Bayesian inference problems.Nonparametric Hypothesis Testing of High-dimensional Clustering With Application to Single-cell RNA Data
Single-cell RNA sequencing studies routinely use clustering to define putative cell types and cell states, yet the observed separation may arise from sampling variability rather than genuine…