Auditing Static Occlusion Ordering Under Prediction-Deviation Budgets

After making a prediction, a data holder may want to release or store fewer fields while keeping that prediction approximately unchanged. One way to choose fields for deletion is to measure their individual effects once and try the least sensitive first. The difficulty is that every accepted deletion changes the context for later trials, and measuring sensitivities consumes queries that could instead explore another path. We audit this trade-off in a cost-tiered deletion procedure that checks each removal against the original prediction. On taxi and movie data, static sensitivity ordering reduces retained cost relative to alphabetical ordering and two random paths given the same query allowance. Under the baseline mask, its relative RMSE increase stays below the chosen tolerance over the first four tested budgets; wider budgets permit larger savings but can exceed that tolerance. Repeated random paths and cost reassignments largely preserve the advantage, whereas alternative masking can reverse it. Static sensitivities therefore provide useful ordering information in these settings, with the benefit depending on the allowed prediction drift and the representation of deleted fields.