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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 1–10 of 704 papers

TitleStatusHype
Foundation models for time series forecasting: Application in conformal prediction—0
The Trilemma of Truth in Large Language ModelsCode0
Deterministic Object Pose Confidence Region Estimation—0
Graph-Structured Feedback Multimodel Ensemble Online Conformal Prediction—0
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal FactualityCode0
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
Valid Selection among Conformal Sets—0
When Can We Reuse a Calibration Set for Multiple Conformal Predictions?—0
One Sample is Enough to Make Conformal Prediction Robust—0
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering—0
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