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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 691–700 of 704 papers

TitleStatusHype
The Pitfalls and Promise of Conformal Inference Under Adversarial AttacksCode0
The Trilemma of Truth in Large Language ModelsCode0
Towards Human-AI Complementarity with Prediction SetsCode0
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal PredictionCode0
Trustworthy Classification through Rank-Based Conformal Prediction SetsCode0
Tube Loss: A Novel Approach for Prediction Interval Estimation and probabilistic forecastingCode0
Uncertainty Estimation for Path Loss and Radio Metric ModelsCode0
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
Uncertainty quantification in automated valuation models with spatially weighted conformal predictionCode0
Uncertainty Quantification of the Virial Black Hole Mass with Conformal PredictionCode0
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