Optimization-Driven Adaptive Experimentation

• URL: http://arxiv.org/abs/2408.04570v2
• Date: Thu, 07 Nov 2024 22:15:01 GMT
• Title: Optimization-Driven Adaptive Experimentation
• Authors: Ethan Che, Daniel R. Jiang, Hongseok Namkoong, Jimmy Wang,
• Abstract summary: Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible, and static designs remain the de facto standard. We present a mathematical programming formulation that can flexibly incorporate a wide range of objectives, constraints, and statistical procedures.
• Score: 7.948144726705323
• License:
• Abstract: Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible, and static designs remain the de facto standard. Focusing on short-horizon ($\le 10$) adaptive experiments, we move away from bespoke algorithms and present a mathematical programming formulation that can flexibly incorporate a wide range of objectives, constraints, and statistical procedures. We formulating a dynamic program based on central limit approximations, which enables the use of scalable optimization methods based on auto-differentiation and GPU parallelization. To evaluate our framework, we implement a simple heuristic planning method ("solver") and benchmark it across hundreds of problem instances involving non-stationarity, personalization, and multiple objectives & constraints. Unlike bespoke methods (e.g., Thompson sampling variants), our mathematical programming framework provides consistent gains over static randomized control trials and exhibits robust performance across problem instances.

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