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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 651–660 of 704 papers

TitleStatusHype
Label Noise Robustness of Conformal Prediction—0
SAFE: Multitask Failure Detection for Vision-Language-Action Models—0
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems—0
Conformal Prediction Regions are Imprecise Highest Density Regions—0
SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation—0
Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers—0
Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction—0
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression—0
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction—0
Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores—0
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