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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 151–160 of 704 papers

TitleStatusHype
FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems—0
From Uncertain to Safe: Conformal Fine-Tuning of Diffusion Models for Safe PDE ControlCode1
Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks—0
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents—0
Conformal Prediction in Hierarchical Classification—0
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach—0
Optimal Transport-based Conformal Prediction—0
Robust Online Conformal Prediction under Uniform Label Noise—0
Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized ModelsCode0
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
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