Image de couverture de livre

Model uncertainty quantification using feature confidence sets for outcome excursions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ren, Junting, Schwartzman, Armin
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Inverse set estimation and inversion of simultaneous confidence intervals
par: Ren, Junting, et autres
Publié: (2022)

Deep set based operator learning with uncertainty quantification
par: Ma, Lei, et autres
Publié: (2025)

Probabilistic neural operators for functional uncertainty quantification
par: Bülte, Christopher, et autres
Publié: (2025)

Conformal confidence sets for biomedical image segmentation
par: Davenport, Samuel
Publié: (2024)

Active multiple matrix completion with adaptive confidence sets
par: Locatelli, Andrea, et autres
Publié: (2026)

Evidential time-to-event prediction with calibrated uncertainty quantification
par: Huang, Ling, et autres
Publié: (2024)

Surrogate modeling for uncertainty quantification in nonlinear dynamics
par: Marelli, S., et autres
Publié: (2025)

Mamba time series forecasting with uncertainty quantification
par: Pessoa, Pedro, et autres
Publié: (2025)

Bayesian meta learning for trustworthy uncertainty quantification
par: Yuan, Zhenyuan, et autres
Publié: (2024)

Distribution free uncertainty quantification in neuroscience-inspired deep operators
par: Garg, Shailesh, et autres
Publié: (2024)

Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification
par: Kim, Jungho, et autres
Publié: (2024)

Generative vs. Discriminative modeling under the lens of uncertainty quantification
par: Argouarc'h, Elouan, et autres
Publié: (2024)

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
par: Schwalbe-Koda, Daniel, et autres
Publié: (2024)

Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
par: Pasparakis, George D., et autres
Publié: (2025)

Locally minimax optimal confidence sets for the best model
par: Kim, Ilmun, et autres
Publié: (2025)

Generative imaging for radio interferometry with fast uncertainty quantification
par: Mars, Matthijs, et autres
Publié: (2025)

Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification
par: Thuy, Arthur, et autres
Publié: (2024)

Variational bagging: a robust approach for Bayesian uncertainty quantification
par: Fan, Shitao, et autres
Publié: (2025)

Conformalized Gaussian processes for online uncertainty quantification over graphs
par: Xu, Jinwen, et autres
Publié: (2025)

Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
par: Zanoni, Andrea, et autres
Publié: (2024)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees
par: Däubener, Sina, et autres
Publié: (2024)

Improved uncertainty quantification for neural networks with Bayesian last layer
par: Fiedler, Felix, et autres
Publié: (2023)

LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics
par: Katz, Sarah, et autres
Publié: (2026)

Active operator learning with predictive uncertainty quantification for partial differential equations
par: Winovich, Nick, et autres
Publié: (2025)

Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge
par: Kumari, Neha, et autres
Publié: (2024)

Probabilistic computation and uncertainty quantification with emerging covariance
par: Ma, Hengyuan, et autres
Publié: (2023)

Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning
par: Chen, Wangqian, et autres
Publié: (2024)

Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
par: Anteneh, Amanuel
Publié: (2025)

Partially stochastic deep learning with uncertainty quantification for model predictive heating control
par: Hannula, Emma, et autres
Publié: (2025)

Stochastic full waveform inversion with deep generative prior for uncertainty quantification
par: Xie, Yuke, et autres
Publié: (2024)

Score-based generative models are provably robust: an uncertainty quantification perspective
par: Mimikos-Stamatopoulos, Nikiforos, et autres
Publié: (2024)

Generative adversarial framework to calibrate excursion set models for the 3D morphology of all-solid-state battery cathodes
par: Furat, Orkun, et autres
Publié: (2025)

Nonparametric extensions of randomized response for private confidence sets
par: Waudby-Smith, Ian, et autres
Publié: (2022)

Mini-batch Submodular Maximization
par: Schwartzman, Gregory
Publié: (2024)

SCoRES: An R Package for Simultaneous Confidence Region Estimates
par: Yu, Zhuoran, et autres
Publié: (2025)

SCoRE Sets: A Versatile Framework for Simultaneous Inference
par: Telschow, Fabian J. E., et autres
Publié: (2023)

Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification
par: Bench, Ciaran
Publié: (2026)

Machine learning-enabled velocity model building with uncertainty quantification
par: Orozco, Rafael, et autres
Publié: (2024)

Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification
par: Hart, J., et autres
Publié: (2024)

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
par: Nannapaneni, Saideep, et autres
Publié: (2025)