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Uncertainty Quantification

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Showing 801–825 of 2366 papers

TitleStatusHype
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models—0
Stochastic Online Conformal Prediction with Semi-Bandit Feedback—0
A Deep Learning Approach to Multi-Fiber Parameter Estimation and Uncertainty Quantification in Diffusion MRI—0
Regression Trees Know Calculus—0
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling—0
Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic SpaceCode1
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation—0
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory—0
Uncertainty quantification by block bootstrap for differentially private stochastic gradient descent—0
Beyond Calibration: Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
Efficient Line Search Method Based on Regression and Uncertainty Quantification—0
Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis—0
Multi-Source Conformal Inference Under Distribution ShiftCode0
Improving Label Error Detection and Elimination with Uncertainty Quantification—0
Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised LearningCode0
The Pitfalls and Promise of Conformal Inference Under Adversarial AttacksCode0
CVAE-SM: A Conditional Variational Autoencoder with Style Modulation for Efficient Uncertainty QuantificationCode0
Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing—0
Do Bayesian imaging methods report trustworthy probabilities?—0
Informativeness of Weighted Conformal PredictionCode0
ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse ProblemsCode0
Uncertainty quantification in metric spaces—0
A design specification for Critical Illness Digital Twins to cure sepsis: responding to the National Academies of Sciences, Engineering and Medicine Report: Foundational Research Gaps and Future Directions for Digital Twins—0
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