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Uncertainty Quantification

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TitleStatusHype
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks—0
General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model—0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy—0
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications—0
Generalizing Large Language Model Usability Across Resource-Constrained—0
Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory—0
General Uncertainty Estimation with Delta Variances—0
Generative emulation of chaotic dynamics with coherent prior—0
Generative Latent Neural PDE Solver using Flow Matching—0
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations—0
Generative Parameter Sampler For Scalable Uncertainty Quantification—0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Generative vs. Discriminative modeling under the lens of uncertainty quantification—0
GeoFUSE: A High-Efficiency Surrogate Model for Seawater Intrusion Prediction and Uncertainty Reduction—0
Geophysical inverse problems with measurement-guided diffusion models—0
Give me (un)certainty -- An exploration of parameters that affect segmentation uncertainty—0
Global Framework for Emulation of Nuclear Calculations—0
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks—0
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning—0
Gradient-enhanced deep neural network approximations—0
Grad-Shafranov equilibria via data-free physics informed neural networks—0
Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks—0
Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation—0
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Graph Structure Learning with Interpretable Bayesian Neural Networks—0
Grassmannian diffusion maps based surrogate modeling via geometric harmonics—0
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data—0
H-AddiVortes: Heteroscedastic (Bayesian) Additive Voronoi Tessellations—0
HappyMap: A Generalized Multi-calibration Method—0
Healing Gaussian Process Experts—0
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning—0
Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning—0
Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs—0
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis—0
Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption—0
Hierarchical Uncertainty Exploration via Feedforward Posterior Trees—0
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization—0
High-Dimensional Confidence Regions in Sparse MRI—0
Spatial best linear unbiased prediction: A computational mathematics approach for high dimensional massive datasets—0
High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling—0
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs—0
Homodyned K-distribution: parameter estimation and uncertainty quantification using Bayesian neural networks—0
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks—0
How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning—0
How disentangled are your classification uncertainties?—0
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