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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 551–575 of 704 papers

TitleStatusHype
Conformal Counterfactual Inference under Hidden Confounding—0
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout—0
Reliable Prediction Intervals with Regression Neural Networks—0
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions—0
Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
Conformal forecasting for surgical instrument trajectory—0
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering—0
Conformal Group Recommender System—0
conformalClassification: A Conformal Prediction R Package for Classification—0
Conformal Inductive Graph Neural Networks—0
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction—0
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series—0
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks—0
Retrain or not retrain: Conformal test martingales for change-point detection—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
Conformalized Answer Set Prediction for Knowledge Graph Embedding—0
Conformalized Credal Regions for Classification with Ambiguous Ground Truth—0
Risk-Sensitive Conformal Prediction for Catheter Placement Detection in Chest X-rays—0
Conformalized Decision Risk Assessment—0
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks—0
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners—0
Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target—0
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
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