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Uncertainty Quantification

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TitleStatusHype
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning—0
From Robustness to Improved Generalization and Calibration in Pre-trained Language Models—0
Fully Nonparametric Bayesian Additive Regression Trees—0
Functional Bayesian Neural Networks for Model Uncertainty Quantification—0
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs—0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—0
Functional SDE approximation inspired by a deep operator network architecture—0
Functional Stochastic Gradient MCMC for Bayesian Neural Networks—0
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions—0
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks—0
Function-Space Regularization for Deep Bayesian Classification—0
Function-Space Variational Inference for Deep Bayesian Classification—0
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review—0
Fuzzy finite element model updating using metaheuristic optimization algorithms—0
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging—0
Gamifying optimization: a Wasserstein distance-based analysis of human search—0
Gaussian Differential Private Bootstrap by Subsampling—0
Gaussian Experts Selection using Graphical Models—0
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation—0
Gaussian processes for dynamics learning in model predictive control—0
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Gaussian process interpolation with conformal prediction: methods and comparative analysis—0
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior—0
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