When building systems that adaptively collect data from multiple groups, the hardness of the problem depends on three factors: your budget, how unevenly uncertainty is distributed across groups, and a new complexity measure (VLC) that quantifies how informative variance changes are—this framewo...
This paper develops a theoretical framework for active learning in multi-group mean estimation, where a learner must allocate a fixed budget of samples across multiple groups to minimize worst-case uncertainty.