Introduction The world cup season is here and off to an interesting start. Whoever thought the reigning champions Germany would be eliminated in the group stage 🙁 For the data scientist within you, lets use this opportunity to do some analysis on soccer clips. With the use of deep learning... Read more

With all the talk about algorithm selection, hyper parameter optimization and so on, you could think that training models is the hardest part of the Machine Learning process. However, in my experience, the really tricky step is to deploy these models safely in a web production environment. In this... Read more

Online conversations, specially around contentious topics, are complex and dynamic. Mapping them is not just a matter of gathering enough data and applying sophisticated algorithms. It’s critical to adjust the map to the questions you want to answer; like models in general, no map is true, but some are... Read more

I’ve spent the last 6 years of my life heavily involved in A/B testing, and other testing methodologies.  Whether it was the performance of an email campaign to drive health outcomes,  product changes, Website changes, the example list goes on. A few of these tests have been full factorial... Read more

We are hiring for reinforcement learning related research at all levels and all MSR labs. If you are interested, apply, talk to me at COLT or ICML, or email me. More generally though, I wanted to lay out a philosophy of research which differs from (and plausibly improves on) the current prevailing mode. Deepmind and OpenAI have popularized an... Read more

It took reading Judea Pearl’s “The Book of Why”, and Jonas Peters’ mini-course on causality, for me to finally figure out why I had this lingering dissatisfaction with modern machine learning. It’s because modern machine learning (deep learning included) is most commonly used as a tool in the service... Read more