jgamper / eeml-keynote

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eeml-keynote

Experimentation

  1. Kohavi, Ron, et al. "Online controlled experiments at large scale." Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013.
  2. Kohavi, Ron, Diane Tang, and Ya Xu. Trustworthy online controlled experiments: A practical guide to a/b testing. Cambridge University Press, 2020.
  3. Hand, David J. "Deconstructing statistical questions." Journal of the Royal Statistical Society: Series A (Statistics in Society) 157.3 (1994): 317-338.
  4. Deng, Alex, Jiannan Lu, and Shouyuan Chen. "Continuous monitoring of A/B tests without pain: Optional stopping in Bayesian testing." 2016 IEEE international conference on data science and advanced analytics (DSAA). IEEE, 2016.
  5. Johari, Ramesh, et al. "Peeking at a/b tests: Why it matters, and what to do about it." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017.
  6. Howard, Steven R., et al. "Time-uniform, nonparametric, nonasymptotic confidence sequences." The Annals of Statistics 49.2 (2021): 1055-1080.
  7. Heide, R. de. Bayesian learning: Challenges, limitations and pragmatics. Diss. Leiden University, 2021.
  8. Liu, C. H., et al. "Datasets for online controlled experiments." arXiv preprint arXiv:2111.10198 (2021).

Interference

  1. Johari, Ramesh, et al. "Experimental design in two-sided platforms: An analysis of bias." Management Science (2022).
  2. Reklaitė, Agnė, and Jevgenij Gamper. "Offline assessment of interference effects in a series of AB tests." The International Conference on Evaluation and Assessment in Software Engineering 2022. 2022.
  3. Ha-Thuc, Viet, et al. "A counterfactual framework for seller-side a/b testing on marketplaces." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020.

Bayesian Optimisation

  1. Bottou, Léon, et al. "Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising." Journal of Machine Learning Research 14.11 (2013).
  2. Mockus, J. (1975). On Bayesian methods for seeking the extremum. In Optimization Techniques IFIP Technical Conference, pages 400–404. Springer.
  3. Daulton, Samuel, Maximilian Balandat, and Eytan Bakshy. "Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement." Advances in Neural Information Processing Systems 34 (2021): 2187-2200.

Marketing Mix Models

  1. Vaver, Jon, and Stephanie Shin-Hui Zhang. "Introduction to the Aggregate Marketing System Simulator." (2017).
  2. Jin, Yuxue, et al. "Bayesian methods for media mix modeling with carryover and shape effects." (2017).
  3. Liley, James, et al. "Model updating after interventions paradoxically introduces bias." International Conference on Artificial Intelligence and Statistics. PMLR, 2021.

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