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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
Language Models with Conformal Factuality Guarantees—0
Conformalized Adaptive Forecasting of Heterogeneous TrajectoriesCode0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Conformal Predictive Programming for Chance Constrained Optimization—0
Self-Calibrating Conformal PredictionCode1
Introspective Planning: Aligning Robots' Uncertainty with Inherent Task AmbiguityCode1
On Temperature Scaling and Conformal Prediction of Deep Classifiers—0
Does confidence calibration improve conformal prediction?Code3
Discounted Adaptive Online Learning: Towards Better RegularizationCode0
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction—0
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