Inferring Causal Impact Using Bayesian Structural Time-Series Models - Brodersen et al. (Google) 2015 Today's paper comes from 'The Annals of Applied Statistics' - not one of my usual sources (!), and a good indication that I'm likely to be well out of my depth again for parts of it. Nevertheless, it addresses a really … Continue reading Inferring Causal Impact Using Bayesian Structural Time-Series Models
Month: March 2016
Graying the Black Box: Understanding DQNs
Graying the Black Box: Understanding DQNs - Zahavy et al. 2016 It's hard to escape the excitement around deep learning these days. Over the last couple of days we looked at some of the lessons learned by Google's machine learning systems teams, including the need to develop ways of getting insights into the predictions made … Continue reading Graying the Black Box: Understanding DQNs
Ad Click Prediction: A View from the Trenches
Ad Click Prediction: a View from the Trenches - McMahan et al. 2013 Yesterday we looked at a tour through the many ways technical debt can creep into machine learning systems. In that paper, the authors mention an automated feature management tool that since its adoption, "has regularly allowed a team at Google to safely … Continue reading Ad Click Prediction: A View from the Trenches
the morning paper 