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arXiv Machine Learning By Mijung Park

Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs

Read the original on arXiv Machine Learning →

Optimizable Model Averaging (OMA) is a black‑box method that assigns nonnegative weights to each candidate model’s predictive probabilities, keeping all model parameters frozen. By minimizing regularized predictive entropy, OMA learns a single scalar coefficient per model to average outputs from heterogeneous architectures. The authors show that appropriate regularization reduces expected calibration error and improves the trade‑off between performance and calibration compared to simple output averaging.

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