YINS Distinguished Lecturer
Elad Hazan, Princeton University
Title: Efficient Optimization for Machine Learning: Beyond Stochastic Gradient Descent
Talk Summary:
In this talk we will describe recent advances in optimization that gave rise to state-of-the-art algorithms in machine learning. While stochastic gradient descent is the workhorse behind the recent deep learning revolution, improving its performance both theoretically and in practice has proven challenging. We will explore recent innovations in stochastic second order methods and adaptive regularization that yield some of the fastest algorithms known.
Bio:
Elad Hazan is a Professor of Computer Science at Princeton University. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. Amongst his contributions are the co-development of the AdaGrad algorithm for training learning machines, and the first sublinear-time algorithms for convex optimization. He is the recipient of (twice) the IBM Goldberg best paper award in 2012 for contributions to sublinear time algorithms for machine learning, and in 2008 for decision making under uncertainty, a European Research Council grant , a Marie Curie fellowship, Google Research Award (twice) and the Bell Labs prize. He serves on the steering committee of the Association for Computational Learning and has been program chair for COLT 2015.
Email Emily Hau (link sends e-mail) to schedule a time to meet with Professor Hazan.