What matters and what doesn't? The machine learning challenges in online ads

What matters and what doesn't? The real machine learning challenges in online advertising.

When I joined Criteo, I was full of ideas about what should be done to improve the system. I quickly realized my academic knowledge was not sufficient to correctly tackle the issues we were facing and that there was much more to it than what is being published in conference papers. This talk will introduce you to the three stages I went through when moving from academia to industry: overconfidence, despair, excitement

Nicolas Le Roux got an MSc in Applied Maths from Ecole Centrale Paris and an MSc in Maths, Learning and Vision from ENS Cachan. He got his PhD in 2008 from University of Montreal where he worked with Yoshua Bengio on neural networks in general and their optimisation in particular. He then moved to Microsoft Research Cambridge to work on generative models of images with John Winn. In 2010, he joined Inria in Francis Bach's team to work on large-scale convex optimisation. Since 2012, he held many positions at Criteo. He was successively in charge of defining the scientific directions of the company, building and managing the research team in Paris, then came back to being a full-time researcher.