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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 351–360 of 704 papers

TitleStatusHype
State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems—0
Exact and Approximate Conformal Inference for Multi-Output Regression—0
Exchangeability, Conformal Prediction, and Rank Tests—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safety—0
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events—0
FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems—0
Statistical Guarantees in Synthetic Data through Conformal Adversarial Generation—0
Correctness Coverage Evaluation for Medical Multiple-Choice Question Answering Based on the Enhanced Conformal Prediction Framework—0
FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair—0
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