The paper evaluates TabPFN, a tabular foundation model, for motor insurance pricing by comparing it to generalized linear models (GLMs) and XGBoost on two motor third‑party liability datasets. Results show that TabPFN does not consistently outperform GLMs: it lags on claim frequency, especially when the in‑context training set is small, but performs competitively on claim severity where data are limited. Additionally, TabPFN’s inference times are substantially longer than those of the baseline methods.
By Bruno Deprez, Wouter Verbeke, Tim Verdonck
arXiv:2508. 13831v4 Announce Type: replace-cross Abstract: Functional data, i.
By Jianbin Tan, Anru R. Zhang
The paper presents Copula Adapted Directed Acyclic Graph (CopDAG), a framework that combines copula models with an ensemble of causal structure discovery methods based on Directed Acyclic Graphs to represent biomedical data. By capturing non‑Gaussian, non‑linear dependencies and stable causal relationships, CopDAG enables clustering of unlabeled biomedical data using K‑means. Across 16 biomedical datasets, CopDAG achieves the highest normalized clustering accuracy and adjusted Rand index among 12 evaluated methods, and it can predict class labels and provide explainable causal visualizations without relying on data annotations.
By Heranga K. Rathnasekara, Norou Diawara, Manar D. Samad
The paper extends the Generalized Naive Bayes (GNB) model to handle continuous explanatory variables. It shows that GNB structure learning depends only on pair copulas of bivariate marginals and can be framed as a matroid, enabling greedy algorithms that minimize Kullback–Leibler divergence. Three model variants are explored—joint Gaussian, Gaussian copula with arbitrary marginals, and fully arbitrary copula and marginals—along with a GNB forest-based model reduction method and empirical comparisons to classical glass‑box classifiers.
By \'Abrah\'am Papp, Botond Szil\'agyi, Edith Alice Kov\'acs
arXiv:2607. 25020v1 Announce Type: new Abstract: Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas.
By Nicholas Andrea Pearson, Francesca Zanello, Davide Russo, Luca Bortolussi, Francesca Cairoli
arXiv:2607. 03487v1 Announce Type: cross Abstract: Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored.
By Alberto Foresti, Ivan Butakov, Alexander Tolmachev, Giulio Franzese, Alexey Frolov, Pietro Michiardi
arXiv:2502. 19460v4 Announce Type: replace-cross Abstract: Dependent censoring occurs when the event time and censoring time are not conditionally independent given the observed covariates.
By Christian Marius Lillelund, Shi-ang Qi, Russell Greiner
arXiv:2605.23632v2 Announce Type: replace
Abstract: We introduce Gaussian Mixture Copula Processes (GMCP), a conditional copula process for irregularly sampled multivariate time series (IMTS) that is...
By Christian Kl\"otergens, Tom Hanika, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi
Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas. Fitting a D-vine requires selecting a copula family and parameter configuration for each pair-copula from a set of candidates encoding different dependence patterns.
arXiv:2403. 00965v2 Announce Type: replace-cross Abstract: Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the performance of machine learning models for early dialysis prediction.
By Hamed Khosravi, Milad Khanchi, Mobina Noori, Srinjoy Das, Abdullah Al-Mamun, Imtiaz Ahmed
arXiv:2511. 17605v2 Announce Type: replace Abstract: Clinical and gene-expression models predict breast cancer outcomes, but simple linear fusion ignores dependence between their risk scores.
By Agnideep Aich, Sameera Hewage, Md Monzur Murshed
The paper introduces a high‑dimensional censored MIDAS logistic regression for forecasting corporate distress, addressing right censoring, high‑dimensional mixed‑frequency predictors, and mixed‑frequency data. It uses inverse probability weighting for censoring, a sparse‑group penalty for estimation, and develops a de‑sparsified estimator with asymptotic theory that accounts for censoring and heavy tails. The method is validated via Monte Carlo simulations and applied to Chinese‑listed firms, with implementation available in the R package Survivalml.
By Wei Miao, Jad Beyhum, Jonas Striaukas, Ingrid Van Keilegom