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Professor Peter Bartlett
Professor
University of California, Berkeley
Peter Bartlett is a professor in the Computer Science Division and Department of Statistics and Associate Director of the Simons Institute for the Theory of Computing at the University of California at Berkeley. His research interests include machine learning and statistical learning theory. He is the co-author, with Martin Anthony, of the book Neural Network Learning: Theoretical Foundations. He has served as an associate editor of the journals Bernoulli, Mathematics of Operations Research, the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, the IEEE Transactions on Information Theory, Machine Learning, and Mathematics of Control Signals and Systems, and as program committee co-chair for COLT and NIPS. He has consulted to a number of organizations, including General Electric, Telstra, SAC Capital Advisors, and Sentient. He has been a Professor in Mathematical Sciences at the Queensland University of Technology (2011-2017), a Miller Institute Visiting Research Professor in Statistics and Computer Science at U.C. Berkeley (Fall 2001), a fellow, senior fellow, and professor in the Research School of Information Sciences and Engineering at the Australian National University's Institute for Advanced Studies (1993-2003), an honorary professor at the University of Queensland and a visiting professor at the University of Paris. He was awarded the Malcolm McIntosh Prize for Physical Scientist of the Year in Australia in 2001, and was chosen as an Institute of Mathematical Statistics Medallion Lecturer in 2008, an IMS Fellow and Australian Laureate Fellow in 2011, and a Fellow of the ACM in 2018. He was elected to the Australian Academy of Science in 2015.
Research Interests:
Adaptive control
Machine Learning
Statistical learning theory
Projects
Publications
Book Chapters
Rubenstein, J.H., Rubinstein B.I.P., & Bartlett P.L.
(2015). Bounding Embeddings of VC Classes into Maximum Classes.
(Vovk, V., Papadopoulos H., & Gammerman A., Ed.).Measures of Complexity. 303-325. doi: 10.1007/978-3-319-21852-6_21
Invited talks, refereed proceedings and other conference outputs
Cheng, X., Roosta-Khorasani F., Palombo S., Bartlett P.L., & Mahoney M.
(2018). FLAG n’ FLARE: Fast Linearly-Coupled Adaptive Gradient Methods.
(Storkey, A., & Perez-Cruz F., Ed.).Conference on Artificial Intelligence and Statistics. 84, 404–414.
Abbasi-Yadkori, Y., Bartlett P.L., Gabillon V., Malek A., & Valko M.
(2018). Best of both worlds: Stochastic & adversarial best-arm identification.
COLT, Conference on Learning Theory. 75, 918-949.
Abbasi-Yadkori, Y., Bartlett P.L., Gabillon V., & Malek A.
(2017). Hit-and-Run for Sampling and Planning in Non-Convex Spaces..
20th International Conference on Artifi- cial Intelligence and Statistics (AISTATS) 2017.
Abbasi-Yadkori, Y., Bartlett P.L., & Gabillon V.
(2017). Near Minimax Optimal Players for the Finite-Time3-Expert Prediction Problem.
31st Conference on Neural Information Processing Systems.
Koolen, W.M., Malek A., Takimoto E., Warmuth M.K., & Bartlett P.L.
(2016). Minimax fixed-design linear regression.
Conference on Learning Theory (COLT2015).
Krichene, W., Bayen A. M., & Bartlett P.L.
(2015). Accelerated Mirror Descent in Continuous and Discrete Time.
Advances in Neural Information Processing Systems 28 (NIPS 2015) .
Malek, A., Koolen W. M., Abbasi-Yadkori Y., & Bartlett P.L.
(2015). Minimax time series prediction.
Advances in Neural Information Processing Systems.
Journal Articles
Abbasi-Yadkori, Y., Bartlett P.L., & Gabillon V.
(2016). Hit-and-run for sampling and planning in non-convex spaces.
arxiv.org.
Gabillon, V., Lazaric A., Ghavamzadeh M., Ortner R., & Bartlett P.L.
(2016). Improved learning complexity in combinatorial pure exploration bandits.
Proceedings of Machine Learning Research . 51, 1004-1012.
Technical reports and unrefereed outputs
Ye, N., & Bartlett P.L.
(2017). Approximate and Stochastic Greedy Optimization.
