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Enhancing Decision-Making: A Framework for Human-AI Uncertainty Collaboration

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Advancing Surface Metrology with Deep Learning and Uncertainty Quantification

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Enhancing AI Reliability: A Dual Approach to Mitigate Overconfident Errors

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Closed-Loop Uncertainty Modeling Improves Autonomous Trajectory Planning

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Quantifying Uncertainty in Machine Learning for High-Stakes Applications

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Ensuring Trustworthy AI Labels: A New Approach to Selective Data Annotation

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Boosting Edge-Cloud LLM Performance with Conformal Sparsification

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Enhancing Trust in Large Language Models with SAFER’s Two-Stage Risk Control

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Proactive Safety: Predicting Robot Failures with Dual-Indicator Monitoring

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Advancing Data Selection with Multi-Condition Conformal Methods

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Enhancing Trust in Large Language Models with Domain-Shift-Aware Uncertainty

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Enhancing Soil Analysis with AI: A Collaborative Approach to Horizon Annotation

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Smarter AI Adaptation: Conformal Prediction for Efficient Learning in Dynamic Environments

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Enhancing Arabic Readability Assessment with Uncertainty-Aware Predictions

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Understanding When Vision-Language Models Say ‘Maybe’: A Deep Dive into Uncertainty

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Unlocking Reliability: How Statistical Methods Bolster Generative AI

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Enhancing Specialized LLM Reliability: A New Approach to Out-of-Domain Detection

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COVER: Ensuring Versatile and Guaranteed AI Sequence Generation

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Anticipating Uncertainty: A New Approach to Time-Series Forecasting with Sudden Shifts

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Predicting AI Reliability: Token-Entropy Conformal Prediction for Language Models

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Uncertainty in AI: The Role of Data Augmentation in Diabetic Retinopathy Prediction

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Enhancing Machine Learning Predictions with Hierarchical Understanding

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AI-Powered Diagnostics for Railway Point Machines

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Zonotopes Unveil a New Era of AI Uncertainty Quantification

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Enhancing LLM Trustworthiness in Multiple-Choice Questions with Statistical Guarantees

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Predicting Railway Track Circuit Failures with Deep Learning

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Enhancing Human-AI Collaboration: A New Approach to Expert Selection with Conformal Prediction

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Measuring AI Confidence: How Sampling Frequency Improves Reliability in Multiple-Choice Questions

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Adaptive AI Framework Streamlines Medical Diagnosis with Cost-Efficiency

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Enhancing Industrial Fault Detection with Calibrated Uncertainty Guarantees

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