The Unofficial Google Data Science Blog

OCTOBER 7, 2015

Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning. Second, even if we could account for all of them, it would still be difficult to predict which ones interact with the A/B treatment (most of them don’t), and what the effect would be.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

The Unofficial Google Data Science Blog

JANUARY 14, 2016

In each case, users engage with the service at will and the service makes available a rich set of possible interactions. But the fact that a service could have millions of users and billions of interactions gives rise to both big data and methods which are effective with big data.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Domino Data Lab

AUGUST 1, 2021

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. Conference on Knowledge Discovery and Data Mining, pp. Guestrin, C., Why should I trust you?:

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139