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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 291–300 of 704 papers

TitleStatusHype
Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting—0
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction—0
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents—0
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks—0
Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression—0
Deterministic Object Pose Confidence Region Estimation—0
Copula-based conformal prediction for Multi-Target Regression—0
Domain Adaptive Skin Lesion Classification via Conformal Ensemble of Vision Transformers—0
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