Historical Thoughts About Modern Prediction

Monday, October 4, 2021 - 4:00pm to 5:00pm

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Benjamin Recht


UC Berkeley

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I'll tell a history of statistical prediction, beginning with Wiener and Rosenblatt and ending with contemporary machine learning. This history will highlight the cyclical rediscovery of pattern recognition and subsequent disillusionment with its shortcomings. I will describe both how much of our theoretical understanding of statistical learning has not deepened for over half a century and trace how the empirical standards of the field arose from technological and social developments. 

Most of the material for this talk will be drawn from a new graduate textbook, "Patterns, Predictions, and Action: A Story About Machine Learning," co-authored with Moritz Hardt.


Benjamin Recht is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He was previously an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Ben received his B.S. in Mathematics from the University of Chicago and received an M.S. and Ph.D. from the MIT Media Laboratory. After completing his doctoral work, he was a postdoctoral fellow in the Center for the Mathematics of Information at Caltech.

Ben is the recipient of a Presidential Early Career Award for Scientists and Engineers, an Alfred P. Sloan Research Fellowship, the 2012 SIAM/MOS Lagrange Prize in Continuous Optimization, the 2014 Jamon Prize, the 2015 William O. Baker Award for Initiatives in Research, and the 2017 NeurIPS Test of Time Award. He is currently on the Editorial Board of the Journal for Machine Learning Research.