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Peter G. Hall Conference 2019: Statistics and Machine Learning, Department of Statistics, UC Davis, May 10-11, 2019. YES X : "Understanding Deep Learning: Generalization, Approximation and Optimization", Eurandom March 19-22, 2019.
Peter Bartlett is a professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics and Head of Google Research Australia. Since 2020, he has been Director of the Foundations of Data Science Institute and Director of the Collaboration on the Theoretical Foundations of Deep Learning.
Peter Bartlett. Professor, EECS and Statistics, UC Berkeley. Verified email at cs.berkeley.edu - Homepage. machine learning statistical learning theory adaptive control. ... J Shawe-Taylor, PL Bartlett, RC Williamson, M Anthony. IEEE transactions on Information Theory 44 (5), 1926-1940, 1998. 746:
Peter Bartlett is a professor in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics at the University of California at Berkeley, Director of the Foundations of Data Science Institute, Director of the Collaboration on the Theoretical Foundations of Deep Learning, and Head of Google Research Australia.
peter@berkeley.edu Research Interests: Machine learning, statistical learning theory, pattern classification, adaptive control, reinforcement learning. Grant Administrator: Natalie Chen nchen86 at berkeley dot edu
http://vcresearch.berkeley.edu/faculty/peter-l-bartlett My research interests are in the areas of machine learning, statistical learning theory, and reinforcement learning. I work on the theoreticalanalysis of computationally efficient methods for large or otherwise complex prediction problems.
Peter Bartlett is Professor of Statistics and Computer Science at UC Berkeley and Principal Scientist at Google DeepMind.
Peter Bartlett is the research director for machine learning at the Simons Institute for the Theory of Computing, and professor in EECS and Statistics at UC Berkeley. He was associate director of the Simons Institute from 2017 to 2022.
Peter Bartlett is a professor in the Division of Computer Science and the Department of Statistics. He is the co-author of the book Learning in Neural Networks: Theoretical Foundations .
Peter L. Bartlett, David P. Helmbold, and Philip M. Long. Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks.