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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 381–390 of 704 papers

TitleStatusHype
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage GuaranteesCode0
Length Optimization in Conformal PredictionCode0
Conformalized Link Prediction on Graph Neural Networks—0
Causal Responder Detection—0
Conformal time series decomposition with component-wise exchangeabilityCode0
Valid Error Bars for Neural Weather Models using Conformal Prediction—0
Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction—0
α-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction—0
Evidential Uncertainty Sets in Deep Classifiers Using Conformal PredictionCode0
Learning Temporal Logic Predicates from Data with Statistical Guarantees—0
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