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Uncertainty Quantification

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TitleStatusHype
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Interpretable machine learning-guided design of Fe-based soft magnetic alloys—0
Interpretable Uncertainty Quantification in AI for HEP—0
Interval Deep Learning for Uncertainty Quantification in Safety Applications—0
IntraSeismic: a coordinate-based learning approach to seismic inversion—0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
Inverted Gaussian Process Optimization for Nonparametric Koopman Operator Discovery—0
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging—0
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging—0
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification—0
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra—0
Is MC Dropout Bayesian?—0
Monte Carlo is a good sampling strategy for polynomial approximation in high dimensions—0
JANET: Joint Adaptive predictioN-region Estimation for Time-series—0
JAPAN: Joint Adaptive Prediction Areas with Normalising-Flows—0
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming—0
Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning—0
Judging LLMs on a Simplex—0
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks—0
KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection—0
Kernel-based Optimally Weighted Conformal Prediction Intervals—0
K-Fold Causal BART for CATE Estimation—0
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection—0
Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions—0
L2M: Practical posterior Laplace approximation with optimization-driven second moment estimation—0
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