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Uncertainty Quantification

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TitleStatusHype
Essential Workers at Risk: An Agent-Based Model (SAFE-ABM) with Bayesian Uncertainty Quantification—0
Gaussian Differential Private Bootstrap by Subsampling—0
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality—0
Multivariate Conformal Selection—0
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding—0
Full-field surrogate modeling of cardiac function encoding geometric variabilityCode0
Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization—0
Model uncertainty quantification using feature confidence sets for outcome excursionsCode0
Interpretable machine learning-guided design of Fe-based soft magnetic alloys—0
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression—0
TopSpace: spatial topic modeling for unsupervised discovery of multicellular spatial tissue structures in multiplex imaging—0
SOFARI-R: High-Dimensional Manifold-Based Inference for Latent Responses—0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems—0
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction—0
Statistical Guarantees in Synthetic Data through Conformal Adversarial Generation—0
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification—0
Conditional Diffusion-Based Retrieval of Atmospheric CO2 from Earth Observing Spectroscopy—0
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning—0
Achieving Distributive Justice in Federated Learning via Uncertainty QuantificationCode0
Position: Bayesian Statistics Facilitates Stakeholder Participation in Evaluation of Generative AI—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
Bayesian Federated Learning for Continual Training—0
Uncertainty quantification of neural network models of evolving processes via Langevin sampling—0
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