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Making the Cut: A Bandit-based Approach to Tiered Interviewing
Candice Schumann · Zhi Lang · Jeffrey Foster · John Dickerson

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #85

Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits? In a tiered interview process, later stages (e.g., in-person visits) are more informative, but also more expensive than earlier stages (e.g., resume screenings). Using accepted hiring models and the concept of structured interviews, a best practice in human resources, we cast tiered hiring as a combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting. The goal is to select a subset of arms (in our case, applicants) with some combinatorial structure. We present new algorithms in both the probably approximately correct (PAC) and fixed-budget settings that select a near-optimal cohort with provable guarantees. We show via simulations on real data from one of the largest US-based computer science graduate programs that our algorithms make better hiring decisions or use less budget than the status quo.

Author Information

Candice Schumann (University of Maryland)
Zhi Lang (University of Maryland, College Park)
Jeffrey Foster (Tufts University)
John Dickerson (University of Maryland)

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