Novel algorithms for efficient learning of distributional regression trees optimized for CRPS and WIS losses via heaps, balanced trees, and Fenwick trees, with competitive performance and conformal prediction applications.
Strictly proper scoring rules, prediction, and estimation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
AlphaEarth embeddings improve out-of-region EMS point-process forecasts 2-6x at 1-2 week histories and 10-20% at longer histories compared to event-only baselines.
citing papers explorer
-
Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts
Novel algorithms for efficient learning of distributional regression trees optimized for CRPS and WIS losses via heaps, balanced trees, and Fenwick trees, with competitive performance and conformal prediction applications.
-
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
AlphaEarth embeddings improve out-of-region EMS point-process forecasts 2-6x at 1-2 week histories and 10-20% at longer histories compared to event-only baselines.