Cover Image

Probabilistic Consistency in Machine Learning and Its Connection to Uncertainty Quantification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Patrone, Paul, Kearsley, Anthony
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Analysis of Diagnostics (Part I): Prevalence, Uncertainty Quantification, and Machine Learning
by: Patrone, Paul N., et al.
Published: (2023)

Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing
by: Patrone, Paul N., et al.
Published: (2025)

Analysis of Diagnostics (Part II): Prevalence, Linear Independence, and Unsupervised Learning
by: Patrone, Paul N., et al.
Published: (2024)

Optimal classification and generalized prevalence estimates for diagnostic settings with more than two classes
by: Luke, Rayanne A., et al.
Published: (2022)

Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity
by: Pernot, Pascal
Published: (2023)

On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning
by: de Lara, Lucas, et al.
Published: (2024)

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
by: Nguyen, Hai-Vy, et al.
Published: (2024)

Scaling Effects and Uncertainty Quantification in Neural Actor Critic Algorithms
by: Georgoudios, Nikos, et al.
Published: (2026)

Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
by: Huynh, Phuoc-Toan, et al.
Published: (2026)

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps
by: Giovanis, Dimitris G, et al.
Published: (2025)

Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux
by: Cazzola, Michele, et al.
Published: (2026)

Uncertainty Quantification for Transformer Models for Dark-Pattern Detection
by: Muñoz, Javier, et al.
Published: (2024)

Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification
by: Alberts, Alex, et al.
Published: (2023)

Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification
by: Pim, Aaron, et al.
Published: (2025)

Probabilities-Informed Machine Learning
by: Rashki, Mohsen
Published: (2024)

Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy
by: Forestano, Roy T., et al.
Published: (2025)

Uncertainty Quantification with Bayesian Higher Order ReLU KANs
by: Giroux, James, et al.
Published: (2024)

Uncertainty Quantification of Antibody Measurements: Physical Principles and Implications for Standardization
by: Patrone, Paul N., et al.
Published: (2024)

Recent Advances in Optimal Transport for Machine Learning
by: Montesuma, Eduardo Fernandes, et al.
Published: (2023)

Scalable Machine Learning Algorithms using Path Signatures
by: Tóth, Csaba
Published: (2025)

Probabilistic Inference and Learning with Stein's Method
by: Liu, Qiang, et al.
Published: (2026)

Structural and Convergence Analysis of Discrete-Time Denoising Diffusion Probabilistic Models
by: Nakano, Yumiharu
Published: (2024)

A Gaussian Comparison Theorem for Training Dynamics in Machine Learning
by: Panahi, Ashkan
Published: (2026)

A Free Probabilistic Framework for Denoising Diffusion Models: Entropy, Transport, and Reverse Processes
by: Das, Swagatam
Published: (2025)

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks
by: Jimenez, Felix, et al.
Published: (2025)

Random Normed k-Means: A Paradigm-Shift in Clustering within Probabilistic Metric Spaces
by: Hemdanou, Abderrafik Laakel, et al.
Published: (2025)

Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
by: Zhang, Rui, et al.
Published: (2023)

A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
by: Pash, Graham, et al.
Published: (2024)

Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning
by: Sorokin, Aleksei G.
Published: (2025)

Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization
by: Jakub, Cameron, et al.
Published: (2023)

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
by: Ajirak, Marzieh, et al.
Published: (2025)

Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
by: Kim, Seyeon, et al.
Published: (2024)

Measurement Uncertainty: Relating the uncertainties of physical and virtual measurements
by: Cramer, Simon, et al.
Published: (2024)

Consistency and Inconsistency in $K$-Means Clustering
by: Blanchard, Moïse, et al.
Published: (2025)

Machine learning-based system reliability analysis with Gaussian Process Regression
by: Zhou, Lisang, et al.
Published: (2024)

Applications of Random Matrix Theory in Machine Learning and Brain Mapping
by: Lawrence, Katrina
Published: (2025)

The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantification
by: Abeyrathna, K. Darshana, et al.
Published: (2024)

Q-Learning under Finite Model Uncertainty
by: Sester, Julian, et al.
Published: (2024)

Probabilistic Conformal Prediction with Approximate Conditional Validity
by: Plassier, Vincent, et al.
Published: (2024)

Statistical Consistency of Discrete-to-Continuous Limits of Determinantal Point Processes
by: Jaquard, Hugo, et al.
Published: (2026)