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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 521–530 of 704 papers

TitleStatusHype
A Cross-Conformal Predictor for Multi-label Classification—0
Android Malware Detection with Unbiased Confidence Guarantees—0
Predictive Inference with Weak Supervision—0
TriQXNet: Forecasting Dst Index from Solar Wind Data Using an Interpretable Parallel Classical-Quantum Framework with Uncertainty Quantification—0
Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective—0
Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems—0
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks—0
Probabilistically robust conformal prediction—0
Aggregating Predictions on Multiple Non-disclosed Datasets using Conformal Prediction—0
A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions—0
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