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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 676–700 of 704 papers

TitleStatusHype
Safety-Critical Control with Offline-Online Neural Network InferenceCode0
Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal PredictionCode0
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal PredictionCode0
Similarity-Navigated Conformal Prediction for Graph Neural NetworksCode0
Sparse Activations as Conformal PredictorsCode0
Split Conformal Prediction under Data ContaminationCode0
Split Localized Conformal PredictionCode0
Stable Conformal Prediction SetsCode0
Stacked conformal predictionCode0
SymmPI: Predictive Inference for Data with Group SymmetriesCode0
Synthetic-Powered Predictive InferenceCode0
Target Strangeness: A Novel Conformal Prediction Difficulty EstimatorCode0
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal PredictionCode0
Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled ExamplesCode0
The Benefit of Being Bayesian in Online Conformal PredictionCode0
The Pitfalls and Promise of Conformal Inference Under Adversarial AttacksCode0
The Trilemma of Truth in Large Language ModelsCode0
Towards Human-AI Complementarity with Prediction SetsCode0
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal PredictionCode0
Trustworthy Classification through Rank-Based Conformal Prediction SetsCode0
Tube Loss: A Novel Approach for Prediction Interval Estimation and probabilistic forecastingCode0
Uncertainty Estimation for Path Loss and Radio Metric ModelsCode0
Uncertainty Quantification in Anomaly Detection with Cross-Conformal p-ValuesCode0
Uncertainty quantification in automated valuation models with spatially weighted conformal predictionCode0
Uncertainty Quantification of the Virial Black Hole Mass with Conformal PredictionCode0
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