Toward Calibrated Mixture-of-Experts Under Distribution Shift
This work addresses the problem of maintaining calibrated predictions in MoE models under distribution shift, which is important for reliable AI systems in dynamic environments.
The paper studies calibration in mixture-of-experts (MoE) models under distribution shift, showing that expert calibration is sufficient for hard-routed but not soft-routed models. They propose an adversarial reweighting method that improves the accuracy-calibration tradeoff across various settings.
Calibration aligns a model's predictive uncertainty with the frequencies of its empirical outcomes and is important for understanding and trusting reported probabilities. Recent work shows that enforcing calibration at the level of individual predictors can improve ensemble accuracy and calibration, with mixture-of-experts (MoE) models showing strong empirical improvements in particular; however, the conditions under which calibration helps MoE are not well understood. In this work, we study how MoE models behave under distribution shift, focusing on how routing mechanisms interact with expert-level calibration. We show that expert calibration is sufficient to ensure calibration of the overall model under a broad class of distribution shifts in hard-routed models, but is insufficient for calibrating soft-routed models. To address this, we propose an adversarial reweighting that penalizes calibration errors of the routed aggregate under distribution shift, and we demonstrate that it improves the accuracy-calibration tradeoff both on average and on difficult subsets of the data, across model classes, prediction tasks, and distribution shifts.