MIDAS Seminar Series Presents: Ryan Adams – Princeton University
September 21 @ 4:00 pm - 5:00 pm
Professor of Computer Science, Princeton University
Director, Undergraduate Certificate in Statistics and Machine Learning, Princeton University
Some New Ideas for Unbiased Gradient Estimation in Optimization
Optimization is at the heart of machine learning, and gradient computation is central to many optimization techniques. Stochastic optimization, in particular, has taken center stage as the principal method of fitting many models, from deep neural networks to variational Bayesian posterior approximations. Generally, one uses data subsampling to efficiently construct unbiased gradient estimators for stochastic optimization, but this is only one possibility. In this talk, I will discuss two alternative approaches to constructing unbiased gradient estimates. The first approach uses randomized truncation of objective functions defined as loops or limits. Such objectives arise in settings ranging from hyperparameter selection, to fitting parameters of differential equations, to variational inference using lower bounds on the log-marginal likelihood. The second approach revisits the Jacobian accumulation problem at the heart of automatic differentiation, observing that it is possible to collapse the linearized computational graph of, e.g., deep neural networks, in a randomized way such that less memory is used but little performance is lost. These projects are joint work with students Alex Beatson, Deniz Oktay, Joshua Aduol, Nick McGreivy, and collaborators at Toronto and Tsinghua.
Bio: Ryan Adams is a machine learning researcher and Professor of Computer Science at Princeton University. Ryan completed his Ph.D. in physics under David MacKay at the University of Cambridge, where he was a Gates Cambridge Scholar and a member of St. John’s College. Following his Ph.D. Ryan spent two years as a Junior Research Fellow at the University of Toronto as a part of the Canadian Institute for Advanced Research. From 2011-2016, he was an Assistant Professor at Harvard University in the School of Engineering and Applied Sciences. In 2015, Ryan sold the company he co-founded, Whetlab, to Twitter and he spent three years in industry at Twitter and Google before joining the faculty at Princeton in 2018. Ryan has won paper awards at ICML, UAI, and AISTATS, received the DARPA Young Faculty Award and the Alfred P. Sloan Fellowship. He also co-hosted the popular Talking Machines podcast.