ML Seminar: Toward the Jet Age of Machine Learning
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.