Probability & Statistics · Mathematics & Statistics
Applications
The newest papers in Applications that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
143 papers, from 4 September 2026 to 5 November 2026: 143 arXiv preprints.
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Newest papers, page 4
Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes
The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads to a teammate scoring.Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
Conditional density estimation describes how the entire distribution of a response changes with covariates, and in imaging studies also with location.Learning joint probabilistic weather forecasts from station observations alone
Assessing compound weather risks requires forecasts representing dependence between variables.Extreme Binary Classification: Extreme Value Theory for Extreme Constraint on False Negative
While binary classification is one of the most extensively studied problems in machine learning, the regime in which the goal is to learn a classifier with an almost zero false negative rate remains…Conformal Prediction for Spatially Dependent Data via Sequential Whitening
Split conformal prediction uses prediction errors on held-out (calibration) data to determine how wide the prediction intervals should be.Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jev
Road safety programs count the coded fields of police crash records, while the officer's narrative, which often records factors the fields omit, is rarely read.Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects
Large language models (LLMs) are increasingly used as judges for automated AI evaluation.Scalable Regularized Vector Multiplicative Error Models for Positive-valued Financial Time Series
The logarithmic multiplicative error model (log-vMEM) has been useful in modeling and forecasting multivariate positive-valued financial time series.Learned Monotone Recurrent Features in Governed Credit Scoring: The Price of the Frame and the Necessity of Macro Conditioning
Regulated credit scoring requires scores monotone non-decreasing in every exposure input.Errors of LLM-Assisted Literature Retrieval in Environmental Science: A Comparison Study of Abstract versus Full-text Based Prompts
Large language models (LLMs) are increasingly used for literature search and synthesis.Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data
Classification problem in the context of highly imbalanced data is a major challenge in many real-world applications (e.g., FinTech, healthcare, etc.).Comparing two approaches for modelling the loss given default of credit cards: Run-off triangles vs regression
The use of run-off triangles (ROTs) is a common industry practice in estimating the loss given default (LGD) risk parameter when predicting credit losses in banking.APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins
Establishing ordinary differential equations (ODEs) describing population data is a fundamental part of mathematical modeling in pharmacology, crucial to developing digital twins.HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning.DeepAJM: Deep Association Joint Model for Irregularly Sampled data
Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of survival outcomes.Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden
Accurate forecasting of pathological outcomes is a central problem in psychology.A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company
Sensing technologies have advanced rapidly across industries ranging from energy to automotive manufacturing.Stance Drift: How AI-mediated Communication Distorts Our Message
Large language models (LLMs) increasingly mediate human communication, from drafting emails to summarizing scientific reports, yet whether they faithfully preserve a speaker's position remains…Low-Rank and Structured Sparse Tensor Decomposition for Anomaly Detection in Multivariate Functional Data
Multivariate functional data arise in many modern manufacturing systems, where multiple sensors record densely sampled process trajectories.CreateScore: Domain-Theory-Informed Bayesian Routing for LLM-Based CV Screening
Large language models (LLMs) can support rubric-based screening of CVs, but applying a high-capability model to every candidate and criterion is costly.