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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 401–410 of 704 papers

TitleStatusHype
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning—0
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering—0
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction—0
Conformal Group Recommender System—0
On the Utility of Prediction Sets in Human-AI Teams—0
On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling—0
On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals—0
On Uncertainty In Natural Language Processing—0
Conformal Inductive Graph Neural Networks—0
Optimal Transport-based Conformal Prediction—0
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