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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1]. References [1] Henning Hohnhold, Deirdre O'Brien, Diane Tang, Focus on the Long-Term: It's better for Users and Business , Proceedings 21st Conference on Knowledge Discovery and Data Mining, 2015. [2]

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 2015): 37-45. [3] Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Cambridge University Press, 2015. [6] 2] Scott, Steven L.

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2] Brendan McMahan et al, "Ad Click Prediction: a View from the Trenches" , Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013. [3]

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

2015) for additional details. Conference on Knowledge Discovery and Data Mining, pp. Neural machine translation by jointly learning to align and translate , ICLR, 2015. Skater uses different techniques depending on the type of the model (e.g. regression, multi-class classification etc.), See Wei et al.

Modeling 139