Image de couverture de livre

Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Toure, Mame Diarra, Stephens, David A.
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Applications Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Singular Bayesian Neural Networks
par: Toure, Mame Diarra, et autres
Publié: (2026)

How Much Is Too Much? Adaptive, Context-Aware Risk Detection in Naturalistic Driving
par: Kalantari, Amir Hossein, et autres
Publié: (2025)

Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
par: Kosko, Matthew, et autres
Publié: (2026)

Contrast-Space Projection for Network Meta-Analysis: An Exact and Invariant Study-Based Decomposition of Direct and Indirect Contributions
par: Wang, Chong, et autres
Publié: (2026)

An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance
par: Ghasemloo, Mohammadmahdi, et autres
Publié: (2024)

Nonparametric IPSS: Fast, flexible feature selection with false discovery control
par: Melikechi, Omar, et autres
Publié: (2024)

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
par: Arpogaus, Marcel, et autres
Publié: (2025)

Approximate Maximum Likelihood Inference for Acoustic Spatial Capture-Recapture with Unknown Identities, Using Monte Carlo Expectation Maximization
par: Wang, Yuheng, et autres
Publié: (2024)

A Bayesian Classification Trees Approach to Treatment Effect Variation with Noncompliance
par: Fisher, Jared D., et autres
Publié: (2024)

Conflict Forecasting via Conformal Prediction for Markov Processes
par: Basarkar, Aditya, et autres
Publié: (2026)

Quantifying How Much Has Been Learned from a Research Study
par: Mikhaeil, Jonas M., et autres
Publié: (2025)

Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments
par: Wang, Qi, et autres
Publié: (2025)

Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets
par: Jonker, Marianne A., et autres
Publié: (2023)

A Bayesian Framework for Causal Analysis of Recurrent Events with Timing Misalignment
par: Oganisian, Arman, et autres
Publié: (2023)

Functional Analysis of Variance for Association Studies
par: Vsevolozhskaya, Olga A., et autres
Publié: (2025)

Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
par: John, Luis H., et autres
Publié: (2020)

Bayesian temporal biclustering with applications to multi-subject neuroscience studies
par: Ricci, Federica Zoe, et autres
Publié: (2024)

Integrative Learning of Dynamically Evolving Multiplex Graphs and Nodal Attributes Using Neural Network Gaussian Processes with an Application to Dynamic Terrorism Graphs
par: Rodriguez-Acosta, Jose, et autres
Publié: (2026)

Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting
par: Zheng, Fangnan, et autres
Publié: (2026)

Nested Atoms Model with Application to Clustering Big Population-Scale Single-Cell Data
par: Chakrabarti, Arhit, et autres
Publié: (2026)

On Model-Based Clustering With Entropic Optimal Transport
par: Mena, Gonzalo
Publié: (2026)

A Nonparametric Adaptive EWMA Control Chart for Binary Monitoring of Multiple Stream Processes
par: Muritala, Faruk, et autres
Publié: (2026)

Multi-Fidelity Quantile Regression
par: Liu, Yixiang, et autres
Publié: (2026)

Group-Aware Matrix Estimation and Latent Subspace Recovery
par: Golubovic, Hamza, et autres
Publié: (2026)

Robust inference for risk heterogeneity under group imbalance
par: Xu, Mengqi, et autres
Publié: (2026)

An intuitive rearranging of the Yates covariance decomposition for probabilistic verification of forecasts with the Brier score
par: Vieira, Bruno Hebling
Publié: (2026)

Integrating Explainable Machine Learning and Mixed-Integer Optimization for Personalized Sleep Quality Intervention
par: Anik, Mahfuz Ahmed, et autres
Publié: (2026)

Stein-Encoder: A White-Box Supervised Encoder via Stein Identities in Multi-Modal Studies
par: Zhang, Jiarui, et autres
Publié: (2026)

Nonparametric efficient inference for network quantile causal effects under partial interference
par: Cheng, Chao, et autres
Publié: (2026)

A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis
par: Minoccheri, Cristian, et autres
Publié: (2026)

Efficient Inference for Noisy LLM-as-a-Judge Evaluation
par: Chen, Yiqun T, et autres
Publié: (2026)

A Bayesian Framework for Post-disruption Travel Time Prediction in Metro Networks
par: Nazemi, Shayan, et autres
Publié: (2026)

Efficient and Debiased Learning of Average Hazard Under Non-Proportional Hazards
par: Meng, Xiang, et autres
Publié: (2026)

Conformal Selective Prediction with General Risk Control
par: Bai, Tian, et autres
Publié: (2026)

Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package
par: Shamsutdinova, Diana, et autres
Publié: (2026)

Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI
par: Xiong, Xin, et autres
Publié: (2026)

Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
par: Pryce, Matthew, et autres
Publié: (2026)

Changepoint Detection As Model Selection: A General Framework
par: Grantham, Michael, et autres
Publié: (2026)

MCMC with Adaptive Principal-Component Transformation: Rotation-Invariant Universal Samplers for Bayesian Structural System Identification
par: Meng, Xianghao, et autres
Publié: (2026)

Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data
par: Zhang, Xinyu, et autres
Publié: (2026)