Multi-variable conformal prediction unifies the design and calibration of prediction sets into a single optimization problem without data splitting while preserving finite-sample coverage guarantees.
Proceedings of the National Academy of Sciences , volume=
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ChaosNetBench is a tunable synthetic benchmark for STGNNs on chaotic lattice dynamics that shows graph models outperform non-graph baselines at high local and global chaos.
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Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting
Multi-variable conformal prediction unifies the design and calibration of prediction sets into a single optimization problem without data splitting while preserving finite-sample coverage guarantees.
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ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics
ChaosNetBench is a tunable synthetic benchmark for STGNNs on chaotic lattice dynamics that shows graph models outperform non-graph baselines at high local and global chaos.