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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 126–150 of 704 papers

TitleStatusHype
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions—0
Probabilistic Conformal Prediction with Approximate Conditional Validity—0
Conditional Conformal Risk Adaptation—0
Are foundation models for computer vision good conformal predictors?—0
Adversarially Robust Conformal Prediction—0
A Cross-Conformal Predictor for Multi-label Classification—0
Concepts and Applications of Conformal Prediction in Computational Drug Discovery—0
Approximating Score-based Explanation Techniques Using Conformal Regression—0
A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning—0
Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers—0
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets—0
Conformal Prediction and Human Decision Making—0
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation—0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI—0
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
Max-Rank: Efficient Multiple Testing for Conformal Prediction—0
Conformal Prediction: A Data Perspective—0
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
Conformal Prediction as Bayesian Quadrature—0
Conformal Prediction for Hierarchical Data—0
Closing the Loop on Runtime Monitors with Fallback-Safe MPC—0
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access—0
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators—0
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