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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 471–480 of 704 papers

TitleStatusHype
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification—0
Safe Adaptive Cruise Control Under Perception Uncertainty: A Deep Ensemble and Conformal Tube Model Predictive Control Approach—0
Safe Merging in Mixed Traffic with Confidence—0
SAFE: Multitask Failure Detection for Vision-Language-Action Models—0
SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation—0
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction—0
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction—0
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms—0
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
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