arXiv AI
Jul 29
Multiclass Classification without Labels via Posterior Simplex Geometry
arXiv:2607. 24943v1 Announce Type: cross Abstract: In many classification problems, reliable instance-level labels are unavailable.
Modular Bayesian Inference for Partially Misspecified Mixtures with Uncertain Class Labels
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arXiv Statistics ML
Aug 26
Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification
arXiv:2608.23960v1 Announce Type: cross Abstract: Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of...
arXiv Machine Learning
Jun 24
Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning
arXiv:2602. 01477v2 Announce Type: replace-cross Abstract: Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks.
arXiv Machine Learning
Aug 12
Hierarchical Empirical-Bayes Naive Bayes: Minimax Smoothing and Calibration with AODE Extension
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.
arXiv Machine Learning
Jun 24
Predictive variational inference: Learn the predictively optimal posterior distribution
arXiv:2410. 14843v4 Announce Type: replace-cross Abstract: Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not meaningful under model misspecification.
arXiv Statistics ML
Oct 7
Concentration and Calibration in Predictive Bayesian Inference
arXiv:2605.00455v2 Announce Type: replace-cross Abstract: Predictive Bayesian inference (PBI) represents a model-and prior-agnostic approach to standard Bayesian inference which allows users to quant...
arXiv Machine Learning
Jun 25
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
arXiv Machine Learning
Jun 19
Variational Consensus Monte Carlo for Bayesian Mixture
arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.
arXiv Machine Learning
Jun 16
Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures
arXiv:2606. 15665v1 Announce Type: cross Abstract: This paper studies the information gap between mixture detection and label recovery in binomial logistic mixtures.
arXiv Statistics ML
5d ago
Two-sample comparison through additive tree models for density ratios
The paper introduces additive tree models for estimating the density ratio between two distributions, using a novel balancing loss that enables training with supervised learning algorithms such as forward‑stagewise optimization and gradient boosting. The loss also functions as a pseudo‑likelihood, allowing generalized Bayesian inference via backfitting samplers for Bayesian additive regression trees, which provides uncertainty quantification. Experiments show that the boosting algorithms achieve high accuracy with low computational cost, and the Bayesian approach offers competitive accuracy while delivering uncertainty estimates, demonstrated on microbiome compositional data.
arXiv Machine Learning
Oct 12
Product Monte Carlo: Expectation Under a Product of Densities
Product Monte Carlo (PMC) is an algorithm that estimates expectations under a product of densities using only Monte Carlo samples from each individual density. By reformulating the intractable product expectation as a regression problem involving computable conditional expectations, PMC provides both point estimates and coherent Bayesian uncertainty quantification. The method achieves higher accuracy, better-calibrated uncertainty, and improved scalability compared to existing techniques, and it does not require density evaluations, enabling its use in simulation‑based inference such as protein image analysis.
arXiv Machine Learning
Jul 27
Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing
arXiv:2412. 12807v4 Announce Type: replace-cross Abstract: Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is high.