Sep 23–26, 2019

Unified Tooling for Machine Learning Interpretability

Harsha Nori (Microsoft), Sameul Jenkins (Microsoft), Rich Caruana (Microsoft)
11:20am12:00pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Ethics

Who is this presentation for?

Data Scientists, Machine Learning Engineers, Software Engineers

Level

Intermediate

Description

In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible (e.g., random forests, boosted trees, and neural nets), and the most intelligible models usually are less accurate (e.g., linear or logistic regression). Interpretability research has focused on minimizing this tradeoff, by developing more accurate interpretable models, and by developing new techniques to explain “blackbox” models.

In this talk, we will walk the audience through a framework for thinking about interpretability, and help them choose the right interpretability method for a variety of real world tasks. We will also present new tooling from Microsoft that helps with all forms of interpretability — both training accurate, interpretable models and understanding “blackbox” models. This toolkit includes the first Python implementation of a powerful new learning algorithm developed here: GA2M.

Prerequisite knowledge

Experience training and evaluating machine learning systems.

What you'll learn

This session will walk you through when and how to use a variety of machine learning interpretability methods, through case studies of real world situations. You will also learn how to use a new interpretability Python toolkit from Microsoft.
Photo of Harsha Nori

Harsha Nori

Microsoft

Harsha is a data scientist at Microsoft. He works on interpretability and privacy for Machine Learning.

Photo of Sameul Jenkins

Sameul Jenkins

Microsoft

Sam is a data scientist at Microsoft. He works on interpretability for machine learning.

Photo of Rich Caruana

Rich Caruana

Microsoft

Rich Caruana is a Principal Researcher at Microsoft Research. Before joining Microsoft, Rich was on the faculty in the Computer Science Department at Cornell University, at UCLA’s Medical School, and at CMU’s Center for Learning and Discovery. Rich’s Ph.D. is from Carnegie Mellon University, where he worked with Tom Mitchell and Herb Simon. His thesis on Multi-Task Learning helped create interest in a new subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), co-chaired KDD in 2007 (with Xindong Wu), and serves as area chair for NIPS, ICML, and KDD. His current research focus is on learning for medical decision making, transparent modeling, deep learning, and computational ecology.

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