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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 11–20 of 704 papers

TitleStatusHype
MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering—0
CertDW: Towards Certified Dataset Ownership Verification via Conformal PredictionCode0
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
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces—0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safety—0
Efficient Robust Conformal Prediction via Lipschitz-Bounded NetworksCode0
Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees—0
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