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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
Online conformal prediction with decaying step sizesCode0
On the Expected Size of Conformal Prediction SetsCode0
On the Role of Surrogates in Conformal Inference of Individual Causal EffectsCode0
Universal distribution of the empirical coverage in split conformal predictionCode0
On training locally adaptive CPCode0
Optimal Conformal Prediction under Epistemic UncertaintyCode0
Parametric Scaling Law of Tuning Bias in Conformal PredictionCode0
Predictive Inference With Fast Feature Conformal PredictionCode0
Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning ApproachCode0
Privacy-Preserving Conformal Prediction Under Local Differential PrivacyCode0
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