Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers: We present prediction sensitivity, an approach for auditing counterfactual fairness while the model is deployed and making predictions.
Maintaining Privacy in Medical Data with Differential Privacy: How can you make use of these datasets without accessing them directly? How can you assure these hospitals that their patients’ data will be protected? Is it even a possibility? We try to answer these questions in this blog post.
Comic – PATE Analysis: A comic representing the PATE Framework.
Deep CARs— Transfer Learning With Pytorch: How can you teach a computer to recognize different car brands? Would you like to take a picture of any car and your phone automatically tells you what the make of the car is?
Population vs Sample , Statistic vs Parameter : Basics of descriptive statistics.