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  1. Peter L. Bartlett. Professor, Computer Science and Statistics. Berkeley AI Research Lab. Director, Foundations of Data Science Institute. Director, Collaboration on the Theoretical Foundations of Deep Learning. ML Research Director, Simons Institute for the Theory of Computing. UC Berkeley. Principal Scientist, Google DeepMind.

    • Publications

      Peter L. Bartlett, David P. Helmbold, and Philip M. Long....

    • Biography

      Peter Bartlett is a professor in the Department of...

    • Achievements

      Ella at four days: Last update: Sun Jun 30 21:49:09 2002

    • Talks

      Peter Bartlett's Talks. Optimization in high-dimensional...

  2. Articles 1–20. ‪Professor, EECS and Statistics, UC Berkeley‬ - ‪‪Cited by 51,813‬‬ - ‪machine learning‬ - ‪statistical learning theory‬ - ‪adaptive control‬.

  3. Peter Bartlett is an American actor. With appearances on shows such as Law & Order and films such as Meet the Parents, Bartlett portrayed Nigel Bartholomew-Smythe on the ABC soap opera, One Life to Live.

  4. Peter Bartlett is a professor of electrical engineering, statistics, and data science at UC Berkeley, and the head of Google Research Australia. He is a leading expert in machine learning and statistical learning theory, and has co-authored a book on neural network learning.

  5. Jul 24, 2022 · Peter L. Bartlett. Professor. Department of Electrical Engineering and Computer Sciences. Department of Statistics. Berkeley AI Research Lab. University of California at Berkeley. Director. Collaboration on the Theoretical Foundations of Deep Learning. Director.

  6. 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.

  7. 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. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning (ICML-18) , volume 80 of Proceedings of ...