Faculty Publications - Peter Bartlett

Books

• A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, Eds., Advances in Large Margin Classifiers, Neural Information Processing Series, Cambridge, MA: MIT Press, 2000.
• M. Anthony and P. L. Bartlett, Neural Network Learning: Theoretical Foundations, Cambridge; New York: Cambridge University Press, 1999.

Book chapters or sections

• 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," 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.
• 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.
• 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.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "Large margin classifiers: Convex loss, low noise, and convergence rates," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 1173-1180.

Articles in journals or magazines

• P. Bartlett and M. Traskin, "AdaBoost is consistent," J. Machine Learning Research, vol. 8, pp. 2347-2368, Oct. 2007.
• 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.
• 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, M. Jordan, and J. D. McAuliffe, "Comment on "Support vector machines with applications"," Statistical Science, vol. 21, no. 3, pp. 341-346, Aug. 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," J. American Statistical Association, vol. 101, no. 473, pp. 138-156, March 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, O. Bousquet, and S. Mendelson, "Local Rademacher complexities," The Annals of Statistics, vol. 33, no. 4, pp. 1497-1537, Aug. 2005.
• G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. Jordan, "Learning the kernel matrix with semidefinite programming," J. Machine Learning Research, vol. 5, pp. 27-72, Dec. 2004.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "[Consistency in Boosting]: Discussion," Annals of Statistics, vol. 32, no. 1, pp. 85-91, Feb. 2004.
• J. Baxter and P. Bartlett, "Infinite-horizon policy-gradient estimation," J. Artificial Intelligence Research, vol. 15, pp. 319-350, Nov. 2001.
• 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.
• P. Bartlett, "The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network," IEEE Trans. Information Theory, vol. 44, no. 2, pp. 525-536, March 1998.

Articles in conference proceedings

• 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.
• 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.

Technical Reports

• J. D. Abernethy, P. Bartlett, A. Rakhlin, and A. Tewari, "Optimal Strategies and Minimax Lower Bounds for Online Convex Games," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2008-19, Feb. 2008.
• A. Rakhlin, A. Tewari, and P. Bartlett, "Closing the Gap between Bandit and Full-Information Online Optimization: High-Probability Regret Bound," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2007-109, Aug. 2007.
• B. I. P. Rubinstein, P. Bartlett, and J. H. Rubinstein, "Shifting: One-Inclusion Mistake Bounds and Sample Compression," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2007-86, June 2007.
• P. Bartlett, E. Hazan, and A. Rakhlin, "Adaptive Online Gradient Descent," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2007-82, June 2007.
• J. D. Abernethy, P. Bartlett, and A. Rakhlin, "Multitask Learning with Expert Advice," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2007-20, Jan. 2007.
• G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan, "Learning the Kernel Matrix with Semi-Definite Programming," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-02-1206, 2002.

Patents

• P. L. Bartlett, A. Elisseeff, and B. Schoelkopf, "Kernels and methods for selecting kernels for use in learning machines," U.S. Patent Application. Nov. 2003.