S-BOMM identifies robust solutions via cross-model consistency in optimization problems with unranked-fidelity models, backed by probabilistic bounds and empirical tests.
Informing university COVID-19 decisions using simple compartmental models,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.
citing papers explorer
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A Consistency-Centric Approach to Set-Based Optimization with Multiple Models of Unranked Fidelity
S-BOMM identifies robust solutions via cross-model consistency in optimization problems with unranked-fidelity models, backed by probabilistic bounds and empirical tests.
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Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
Mixture-of-experts fusing multiple pretrained forecasters achieves strongest performance on influenza time series, with pretraining gains largest at longer horizons when domain-aligned and LLM methods underperforming.