Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization
Published in Transactions of Machine Learning Research, 2022
This paper develops methods for bridging offline and online experimentation through constraint active search. We propose algorithms that enable effective performance optimization in deployed systems by combining insights from both offline analysis and online experimentation, providing practical solutions for real-world deployment scenarios.
Recommended citation: Komiyama, J., Malkomes, G., Cheng, B., & McCourt, M. (2022). “Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization.” Transactions of Machine Learning Research.
Recommended citation: Komiyama, J., Malkomes, G., Cheng, B., & McCourt, M. (2022). "Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization." Transactions of Machine Learning Research.
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