Title: Weighted Deep Ensemble Under Misspecification
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Deep Ensembles;  Weighted Averaging, Misspecification; Asymptotic optimality;

Abstract: Deep neural networks are supported by the universal approximation theorem, which guarantees that sufficiently large architectures can approximate smooth functions. In practice, however, this guarantee holds only under restrictive conditions, and violations of these conditions give rise to model misspecification. We categorize such misspecification into three sources: variable misspecification, arising from insufficiently informative features; structural misspecification, stemming from the limited width and depth of networks that cannot fully capture the underlying complexity; and inherent misspecification, occurring when the true model possesses properties such as discontinuities that cannot be faithfully represented. To mitigate the impact of these forms of misspecification, ensemble methods have become a common strategy for enhancing predictive performance. However, standard ensembles composed of identically architected and equally weighted models may suffer from "collective blindness", where shared errors are amplified and lead to systematically biased predictions with high confidence. To mitigate this issue, we introduce weighted deep ensemble method that  learns the optimal weights. We prove that our method provably attains the convergence rate of the best single model in the ensemble and asymptotically achieves oracle-level predictive risk. To the best of our knowledge, this is the first work to provide rigorous theoretical guarantees for weighted deep ensemble under both well-specified and misspecified settings.

TL;DR: We provide rigorous theoretical guarantees for weighted deep ensembles under both well-specified and misspecified settings.