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Paul Bartlett is a distinguished professor in the Departments of Electrical Engineering and Computer Sciences (EECS) and Statistics at the University of California, Berkeley. He is also a Senior Staff Research Scientist at Google Research. His research interests are in the statistical and algorithmic aspects of machine learning, including reinforcement learning, deep learning theory, statistical learning theory, and online learning. He has made significant contributions to understanding the generalization capabilities of learning algorithms, particularly in areas like PAC-Bayesian analysis and the theory of neural networks. Before joining UC Berkeley, he held positions at the Australian National University. He is highly cited and has influenced many researchers in the field.
Paul Bartlett's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Elected for his significant contributions to the theory and application of statistics, particularly in machine learning.
Recognized for his fundamental contributions to statistical learning theory and machine learning.
Awarded for papers presented at past NeurIPS conferences that have had a lasting and significant impact on the field of machine learning.
His work on topics like Rademacher complexity, PAC-Bayesian bounds, and the analysis of large margin classifiers has been foundational.
Authored an influential textbook providing a rigorous theoretical treatment of learning in neural networks.
Ravens Wood School for Boys - Year 1980
Bromley Adult Education College
Lewisham College
Ravens Wood School for Boys
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ENGIE Impact, an ENGIE Group company, partners with businesses, cities, and governments around the world to accelerate their sustainability transformation. They provide data-driven insights, expert advisory services, and technology solutions to help organizations reduce resource consumption (energy, water, waste), decarbonize their operations, and implement long-term sustainability strategies. Their goal is to bridge the gap between sustainability ambitions and tangible, economically viable results.
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