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Uncertainty Quantification

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TitleStatusHype
Evidential Active Recognition: Intelligent and Prudent Open-World Embodied Perception—0
Beta-Sorted Portfolios—0
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference—0
Hidden Markov Neural Networks—0
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription—0
Better Uncertainty Quantification for Machine Translation Evaluation—0
DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems—0
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation—0
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization—0
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory—0
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors—0
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
Beyond mirkwood: Enhancing SED Modeling with Conformal Predictions—0
Efficient Conformal Prediction under Data Heterogeneity—0
Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking—0
Beyond Non-Expert Demonstrations: Outcome-Driven Action Constraint for Offline Reinforcement Learning—0
An asymptotic analysis of distributed nonparametric methods—0
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Uncertainty Quantification and Exploration for Reinforcement Learning—0
Efficient Line Search Method Based on Regression and Uncertainty Quantification—0
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning—0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation—0
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling—0
Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processes—0
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network—0
Adaptive Uncertainty Quantification for Scenario-based Control Using Meta-learning of Bayesian Neural Networks—0
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields—0
Black-box Uncertainty Quantification Method for LLM-as-a-Judge—0
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering—0
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions—0
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators—0
Advances in Surrogate Modeling for Biological Agent-Based Simulations: Trends, Challenges, and Future Prospects—0
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks—0
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification—0
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data—0
Emulator-based global sensitivity analysis for flow-like landslide run-out models—0
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems—0
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning—0
End-to-end Conditional Robust Optimization—0
End-to-End Probabilistic Framework for Learning with Hard Constraints—0
Enhanced multi-fidelity modelling for digital twin and uncertainty quantification—0
Bayesian Federated Learning for Continual Training—0
Harnessing On-Machine Metrology Data for Prints with a Surrogate Model for Laser Powder Directed Energy Deposition—0
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