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Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners.

, , , , , , and . KDD, page 2156-2164. ACM, (2019)

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Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments., , , , and . WSDM, page 877-880. ACM, (2020)A/B Testing at Scale: Accelerating Software Innovation., , , , and . WWW (Companion Volume), page 1299-1300. ACM, (2019)Enterprise-Level Controlled Experiments at Scale: Challenges and Solutions., , , , , , and . SEAA, page 29-37. IEEE, (2019)Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments., , , and . KDD, page 3189-3190. ACM, (2019)A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments., , , and . KDD, page 1427-1436. ACM, (2017)Pitfalls of long-term online controlled experiments., , , , and . IEEE BigData, page 1367-1376. IEEE Computer Society, (2016)A/B Testing at Scale: Accelerating Software Innovation., , , , , and . SIGIR, page 1395-1397. ACM, (2017)Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners., , , , , , and . KDD, page 2156-2164. ACM, (2019)Computing hermite forms of polynomial matrices., and . ISSAC, page 155-162. ACM, (2011)Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments., , , and . WWW (Companion Volume), page 317-319. ACM / IW3C2, (2020)