Inferring Causal Impact Using Bayesian Structural Time-Series Models

March 3, 2016 ~ Adrian Colyer ~ 4 Comments

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

Graying the Black Box: Understanding DQNs

March 2, 2016 ~ Adrian Colyer ~ 13 Comments

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

Ad Click Prediction: A View from the Trenches

March 1, 2016 ~ Adrian Colyer ~ 2 Comments

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

the morning paper

a random walk through Computer Science research, by Adrian Colyer
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Month: March 2016

Inferring Causal Impact Using Bayesian Structural Time-Series Models

Graying the Black Box: Understanding DQNs

Ad Click Prediction: A View from the Trenches