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Selection under Overlapping Requirements: Sharp Local Audits
Sharp bounds on how far institutions must search for collective improvements when selecting fixed-size teams under overlapping expertise requirements and a common priority order.
Research in progress
Sharp bounds on how far institutions must search for collective improvements when selecting fixed-size teams under overlapping expertise requirements and a common priority order.
An anchor characterization of stable choices and an empirical comparison of PCS and SF-CDA on nationwide admissions data.
A framework connecting predictive gains from cross-retailer data pooling to competition, coalition stability, and revenue allocation.
A scalable one-step method for removing selected data owners’ influence without full retraining.
Matching mechanisms under shared regional resource constraints.