Toward the Jet Age of Machine Learning

Seminar
Friday, February 21, 2020
11:00 AM to 12:00 PM
EER 1.516
Free and open to the public

Machine learning today bears resemblance to the field of aviation soon after the Wright Brothers’ pioneering flights in the early 1900s. It took half a century of aeronautical engineering advances for the ‘Jet Age’ (i.e., commercial aviation) to become a reality. Similarly, machine learning (ML) is currently experiencing a renaissance, yet fundamental barriers must be overcome to fully unlock the potential of ML-powered technology. In this talk, I describe our work to help democratize ML by tackling barriers related to scalability, privacy, and safety. In the context of scalability and privacy, I discuss theoretically principled, privacy-preserving approaches to federated learning (i.e., learning over massive networks of edge devices) that rely on novel connections to gradient-based meta-learning. In the context of safety, we reduce the gap between model transparency and model accuracy via a novel model family of interpretable random forests that also serves as a state-of-the-art black-box explanation system.

Speaker

Ameet Talwalkar

Carnegie Mellon University

Ameet Talwalkar is an assistant professor in the Machine Learning Department at CMU, and also co-founder and Chief Scientist at Determined AI. His interests are in the field of statistical machine learning. His current work is motivated by the goal of democratizing machine learning, with a focus on topics related to scalability, automation, fairness, and interpretability of learning algorithms and systems. He led the initial development of the MLlib project in Apache Spark, is a co-author of the textbook ‘Foundations of Machine Learning’ (MIT Press), and created an award-winning edX MOOC on distributed machine learning. He also helped to create the MLSys conference, serving as the inaugural Program Chair in 2018, General Chair in 2019, and currently as President of the MLSys Board.