Alex Gilotte, researcher at the Criteo AI Lab, details how counterfactual reasoning may be applied to evaluate a recommender system. The first posts introduce the topic and the intuitions behind the mathematics of counterfactual reasoning, and the later posts will detail how we dealt at Criteo with the practical challenges which arise when using those methods.
- 9 papers accepted to @NeurIPS20 co-authored by researchers from Criteo AI Lab
- New post by O. Koch: “The Trade-Offs of Large-Scale Machine Learning: the price of time”
- Best student ML paper award runner-up @ECMLPKDD
- DeepR — Training TensorFlow Models for Production
- One paper accepted at ECCV2020: “Do not mask what you do not need to mask: a parser free Virtual Try-on”,