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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 251–275 of 704 papers

TitleStatusHype
Calibrating AI Models for Wireless Communications via Conformal Prediction—0
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction—0
Conformal Regression in Calorie Prediction for Team Jumbo-Visma—0
Distribution-Free Finite-Sample Guarantees and Split Conformal Prediction—0
Distribution-free Conformal Prediction for Ordinal Classification—0
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction—0
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
Conformalized Link Prediction on Graph Neural Networks—0
A Conformal Approach to Feature-based Newsvendor under Model Misspecification—0
Distribution-Free Guarantees for Systems with Decision-Dependent Noise—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Android Malware Detection with Unbiased Confidence Guarantees—0
Deterministic Object Pose Confidence Region Estimation—0
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging—0
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks—0
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
Calibrated Physics-Informed Uncertainty Quantification—0
Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification—0
Development and Evaluation of Conformal Prediction Methods for QSAR—0
Distribution-Free Matrix Prediction Under Arbitrary Missing Pattern—0
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