Peter Bartlett
Professor
Info Links
Research Areas
- Artificial Intelligence (AI), machine learning, statistical learning theory
- Control, Intelligent Systems, and Robotics (CIR)
Research Centers
Teaching Schedule
Spring 2018
- CS 198-82. Machine Learning DeCal, Th 5:00PM - 6:59PM, Genetics & Plant Bio 100
Biography
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. He has served as associate editor of the journals Machine Learning, Mathematics of Control Signals and Systems, the Journal of Machine Learning Research, the Journal of Artificial Intelligence Research, and the IEEE Transactions on Information Theory. He was awarded the Malcolm McIntosh Prize for Physical Scientist of the Year in Australia for his work in statistical learning theory. He was a Miller Institute Visiting Research Professor in Statistics and Computer Science at U.C. Berkeley in Fall 2001, and 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). He is also an honorary professor in the Department of Computer Science and Electrical Engineering at the University of Queensland.
Selected Publications
- F. Hedayati and P. Bartlett, "citeKey, The Optimality of {J}effreys Prior for Online DensityEstimation and the Asymptotic Normality of MaximumLikelihood Estimators," in Proceedings of the Conference onLearning Theory (COLT2012), Vol. 23, 2012, pp. 7.1-7.13.
- F. Hedayati and P. Bartlett, "Exchangeability Characterizes Optimality of SequentialNormalized Maximum Likelihood and {Bayesian} Prediction with {Jeffreys}Prior," in Proceedings of The Fifteenth International Conference on Artificial Intelligence and Statistics(AISTATS), M. Girolami and N. Lawrence, Eds., 2012.
- A. Barth, B. I. P. Rubinstein, M. Sundararajan, J. C. Mitchell, D. Song, and P. Bartlett, "A Learning-Based Approach to Reactive Security," in Financial Cryptography and Data Security '10. Fourteenth International Conference, 2010.
- A. Tewari and P. Bartlett, "Optimistic linear programming gives logarithmic regret for irreducible MDPs," in Advances in Neural Information Processing Systems 20: Proc. of the 21st Annual Conf. (NIPS 2007), D. Koller, Y. Singer, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 20, Cambridge, MA: MIT Press, 2008.
- P. Bartlett, E. Hazan, and A. Rakhlin, "Adaptive online gradient descent," in Advances in Neural Information Processing Systems 20: Proc. of the 21st Annual Conf. (NIPS 2007), D. Koller, Y. Singer, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 20, Cambridge, MA: MIT Press, 2008.
- P. Bartlett and M. Traskin, "AdaBoost is consistent," J. Machine Learning Research, vol. 8, no. 10, pp. 2347-2368, Oct. 2007.
- B. I. P. Rubinstein, P. Bartlett, and J. H. Rubinstein, "Shifting, one-inclusion mistake bounds and tight multiclass expected risk bounds," in Advances in Neural Information Processing Systems 19: Proc. of the 20th Annual Conf. (NIPS 2006), B. Scholkopf, J. Platt, and T. Hoffman, Eds., Advances in Neural Information Processing Systems, Vol. 19, Cambridge, MA: MIT Press, 2007, pp. 1193-1200.
- P. Bartlett and A. Tewari, "Sample complexity of policy search with known dynamics," in Advances in Neural Information Processing Systems 19: Proc. of the 20th Annual Conf. (NIPS 2006), B. Scholkopf, J. Platt, and T. Hoffman, Eds., Advances in Neural Information Processing Systems, Vol. 19, Cambridge, MA: MIT Press, 2007, pp. 97-104.
- J. Abernethy, P. Bartlett, and A. Rakhlin, "Multitask learning with expert advice," in Learning Theory: Proc. 20th Annual Conf. on Learning Theory (COLT 2007), N. H. Bshouty and C. Gentile, Eds., Lecture Notes in Computer Science: Artificial Intelligence, Vol. 4539, Berlin, Germany: Springer-Verlag, 2007, pp. 484-498.
- A. Tewari and P. Bartlett, "Bounded parameter Markov decision processes with average reward criterion," in Learning Theory: Proc. 20th Annual Conf. on Learning Theory (COLT 2007), N. H. Bshouty and C. Gentile, Eds., Lecture Notes in Computer Science: Artificial Intelligence, Vol. 4539, Berlin, Germany: Springer-Verlag, 2007, pp. 263-277.
- A. Rakhlin, J. Abernethy, and P. Bartlett, "Online discovery of similarity mappings," in Proc. 24th Intl. Conf. on Machine Learning (ICML-2007), Z. Ghahramani, Ed., ACM International Conference Proceeding Series, Vol. 227, New York, NY: The Association for Computing Machinery, Inc., 2007, pp. 767-774.
- A. Tewari and P. Bartlett, "On the consistency of multiclass classification methods," J. Machine Learning Research: Special Topic on the Conference on Learning Theory 2005, vol. 8, no. 5, pp. 1007-1025, May 2007.
- P. Bartlett and A. Tewari, "Sparseness vs estimating conditional probabilities: Some asymptotic results," J. Machine Learning Research, vol. 9, no. 4, pp. 775-790, April 2007.
- D. Rosenberg and P. Bartlett, "The Rademacher complexity of co-regularized kernel classes," in Proc. 11th Intl. Conf. on Artificial Intelligence and Statistics (AISTAT 2007), M. Meila and X. Shen, Eds., Vol. 2, Cambridge, MA: Journal of Machine Learning Research/MIT, 2007, pp. 396-403.
- P. Bartlett and S. Mendelsohn, "Empirical minimization," Probability Theory and Related Fields, vol. 135, no. 3, pp. 311-334, July 2006.
- P. Bartlett, M. Jordan, and J. D. McAuliffe, "Convexity, classification, and risk bounds," J. American Statistical Association, vol. 101, no. 473, pp. 138-156, March 2006.
- P. Bartlett, O. Bousquet, and S. mendelson, "Local Rademacher complexities," The Annals of Statistics, vol. 33, no. 4, pp. 1497-1537, Aug. 2005.
- J. Baxter and P. Bartlett, "Infinite-horizon policy-gradient estimation," J. Artificial Intelligence Research, vol. 15, pp. 319-350, Nov. 2001.
- M. Anthony and P. L. Bartlett, Neural Network Learning: Theoretical Foundations, Cambridge; New York: Cambridge University Press, 1999.
- R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, "Boosting the margin: A new explanation for the effectiveness of voting methods," The Annals of Statistics, vol. 26, no. 5, pp. 1651-1686, May 1998.