Peter Bartlett

Professor

Research Areas

• Artificial Intelligence (AI), machine learning, statistical learning theory
• Control, Intelligent Systems, and Robotics (CIR)

Research Centers

• Center for Intelligent Systems (CIS)

Teaching Schedule (Spring 2008)

• CS C281B. Advanced Topics in Learning and Decision Making, T T 12:30-2:00P, 310 Soda

Biography

Peter Bartlett is a professor in the Division of Computer Science and 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

• 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, pp. 2347-2368, Oct. 2007.
• P. Bartlett and M. Traskin, "AdaBoost is consistent," 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, Vol. 19, Cambridge, MA: MIT Press, 2007, pp. 105-112.
• 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, vol. 8, pp. 1007-1025, May 2007.
• P. Bartlett and A. Tewari, "Sparseness vs estimating conditional probabilities: Some asymptotic results," J. Machine Learning Research, vol. 9, 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. Mendelson, "Discussion of "2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization" by V. Koltchinskii"," The Annals of Statistics, vol. 34, no. 6, pp. 2657-2663, Dec. 2006.
• 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," Journal of the 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.
• P. Bartlett, M. Collins, B. Taskar, and D. McAllester, "Exponentiated gradient algorithms for large-margin structured classification," in Advances in Neural Information Processing Systems 17: Proc. of the 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 113-120.
• R. Jimenez-Rodriguez, N. Sitar, and P. Bartlett, "Maximum likelihood estimation of trace length distribution parameters using the EM algorithm," in Prediction, Analysis and Design in Geomechanical Applications: Proc. 11th Intl. Conf. of Computer Methods and Advances in Geomechanics (IACMAG), G. Barla and M. Barla, Eds., Bologna, Italy: Patron Editore, 2005, pp. 619-626.
• A. Tewari and P. Bartlett, "Winner, Student Paper Award: On the consistency of multiclass classification methods," in Learning Theory: Proc. of the 18th Annual Conf. on Learning Theory (COLT 2005), P. Auer and R. Meir, Eds., Lecture Notes in Computer Science: Artificial Intelligence, Vol. 3559, Berlin, Germany: Springer-Verlag, 2005, pp. 143-157.
• J. Baxter and P. Bartlett, "Infinite-horizon policy-gradient estimation," J. Artificial Intelligence Research, vol. 15, pp. 319-350, Nov. 2001.