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.
arXiv:1906.02590v2 Announce Type: replace-cross Abstract: This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods...
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.
arXiv:2607.28844v2 Announce Type: replace-cross Abstract: Bayesian Additive Regression Trees (BART) have shown state-of-the-art performance in both prediction and causal inference problems. Previous...
arXiv:2506. 01075v2 Announce Type: replace-cross Abstract: The Boolean Fourier representation has been widely used in learning theory, particularly for learning Disjunctive Normal Form (DNF) under uniform and product distributions.
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...
arXiv:2608. 11162v1 Announce Type: new Abstract: The Naive Bayes (NB) classifier remains a standard choice for categorical data, yet its widely used smoothing rules, such as Laplace, Lidstone, Krichevsky-Trofimov, and the $m$-estimate, all prescribe a fixed smoothing strength that ignores feature cardinality, sample size, and class imbalance, inducing a non-vanishing bias on modern high-cardinality tabular data.
The paper introduces a framework that learns the kernel used in kernel methods through alignment, leveraging the Collaborative Learning and Inference (CLaI) approach. It demonstrates that CLaI can be interpreted as a kernel alignment process and that its inference stage is equivalent to kernel Bayes classification with Parzen-window density estimation. By replacing cosine similarity with a learned Mahalanobis distance, the authors extend CLaI to multiclass classification, achieving higher accuracy, faster convergence, and lower calibration error on datasets such as CIFAR-10, PathMNIST, and SleepEDF, while also showing connections to Gaussian processes and competitive calibration in sepsis prediction.
arXiv:2405. 15768v2 Announce Type: replace-cross Abstract: In this paper, we address the classification of instances represented by distributions on a vector space rather than single points.
arXiv:2606. 13984v1 Announce Type: cross Abstract: Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures.
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.
arXiv:2607. 04977v1 Announce Type: new Abstract: Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift.
arXiv:2603.16621v2 Announce Type: replace Abstract: We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simp...