A Constrained Arm inside a Partitioned Regression Domain
Abstract
Constrained best-arm learning and partition regression organize feasibility evidence differently. Their comparison should preserve the same terminal admissibility requirement and check local boundary candidates independently. A partition can conceal an infeasible pocket, while posterior objective attraction can distract from unresolved constraints. Resource accounting should distinguish screening from precise objective comparison. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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