EAPO adapts wildfire models to new environments via k-nearest neighbor data retrieval and hybrid fine-tuning that emphasizes rare extreme events, achieving ROC-AUC 0.7310 on real data.
citation dossier
Miller, Christopher B
1Pith papers citing it
1reference links
cs.LGtop field · 1 papers
UNVERDICTEDtop verdict bucket · 1 papers
why this work matters in Pith
Pith has found this work in 1 reviewed paper. Its strongest current cluster is cs.LG (1 papers). The largest review-status bucket among citing papers is UNVERDICTED (1 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Environment-Adaptive Preference Optimization for Wildfire Prediction
EAPO adapts wildfire models to new environments via k-nearest neighbor data retrieval and hybrid fine-tuning that emphasizes rare extreme events, achieving ROC-AUC 0.7310 on real data.