Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
The Journal of Machine Learning Research , volume=
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AeroSense predicts regional air traffic flow from instantaneous aircraft states rather than historical time-series aggregates, showing accuracy gains especially in dense traffic.
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Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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Unlocking air traffic flow prediction through microscopic aircraft-state modeling
AeroSense predicts regional air traffic flow from instantaneous aircraft states rather than historical time-series aggregates, showing accuracy gains especially in dense traffic.