Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming Finale Doshi-Velez, a Professor of Computer Science at the Harvard School of Engineering and Applied Sciences. She will be presenting work entitled Bayesian Or-of-And Models for Interpretable Classification.  Please find the abstract below and on the website.

As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.  See you all there!

-- Anton

Title: Bayesian Or-of-And Models for Interpretable Classification

Abstract: Interpretability is an important factor for models to be used and trusted in many applications.  Disjunctive normal forms, also known as or-of-and models, are models with classification rules of the form "Predict True if (A and B) or (A and C) or D."  They are an appealing form of classifier because one can easily trace how a classification decision was made, and has some basis in human decision-making.  In this talk, I will talk about a Bayesian approach to learning or-of-and models and describe an application to context-aware recommender systems.