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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 101–110 of 704 papers

TitleStatusHype
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees—0
Valid Selection among Conformal Sets—0
When Can We Reuse a Calibration Set for Multiple Conformal Predictions?—0
One Sample is Enough to Make Conformal Prediction Robust—0
CertDW: Towards Certified Dataset Ownership Verification via Conformal PredictionCode0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering—0
A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions—0
Conformal Safety Shielding for Imperfect-Perception Agents—0
SAFE: Multitask Failure Detection for Vision-Language-Action Models—0
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