- Bernardi, Lucas, Themistoklis Mavridis, and Pablo Estevez. "150 successful machine learning models: 6 lessons learned at booking. com." Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2019. pdf
- Bottou, Léon, et al. "Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising." Journal of Machine Learning Research 14.11 (2013). pdf
- Xu, Ziyu, Ruodu Wang, and Aaditya Ramdas. "A unified framework for bandit multiple testing." Advances in Neural Information Processing Systems 34 (2021). pdf
- Yang, Jeremy, et al. "Targeting for long-term outcomes." arXiv preprint arXiv:2010.15835 (2020). pdf
- 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. pdf
- Letham, B., et al. "Constrained Bayesian optimization with noisy experiments. arXiv, 1–20." arXiv preprint arxiv:1706.07094 (2017). pdf
Other resources:
- To learn about formulating applied machine learning problems, consider taking this course (its free): Causal Diagrams: Draw Your Assumptions Before Your Conclusions
- Analogical thinking and story telling are important to recognise opportunities for algorithmic solutions, I'd strongly recommend the following book as a fun read: Christian, Brian, and Tom Griffiths. Algorithms to live by: The computer science of human decisions. Macmillan, 2016.
- David Ziganto on Simulated Datasets for Faster ML Understanding link
- David Robinson on Scientific Debt, scientific debt is what we incur when we apply supervised learning algorithms while poorly formulating the problem link
- Edoardo Conti on Offline Policy Evaluation: Run fewer, better A/B tests link
- Sean Taylor on Designing and Evaluating metrics link