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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 691–700 of 704 papers

TitleStatusHype
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction—0
Cautious Deep Learning—0
conformalClassification: A Conformal Prediction R Package for Classification—0
Conformal Prediction in Learning Under Privileged Information Paradigm with Applications in Drug Discovery—0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
Model-Robust Counterfactual Prediction MethodCode0
Universal probability-free prediction—0
Conformal Predictors for Compound Activity Prediction—0
Criteria of efficiency for conformal prediction—0
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