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

• J. Abernethy, P. Bartlett, A. Rakhlin, and A. Tewari, "Optimal strategies and minimax lower bounds for online convex games," in Learning Theory: Proc. 21st Annual Conf. (COLT 2008), R. A. Servedio and T. Zhang, Eds., Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag, 2008, pp. 415-424.
• P. Bartlett, V. Dani, T. P. Hayes, S. Kakade, A. Rakhlin, and A. Tewari, "High-probability regret bounds for bandit online linear optimization," in Learning Theory: Proc. 21st Annual Conf. (COLT 2008), R. A. Servedio and T. Zhang, Eds., Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag, 2008, pp. 335-342.
• 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, "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

• W. Mou, M. Yi-An, M. Wainwright, P. Bartlett, and M. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm," Journal of Machine Learning Research, vol. 22, pp. 1-48, March 2021.
• A. Barth, B. I. P. Rubinstein, M. Sundararajan, J. C. Mitchell, D. Song, and P. Bartlett, "A Learning-Based Approach to Reactive Security.," IEEE Transactions on Dependable and Secure Computing, vol. 9, no. 4, pp. 482-493, July 2012.
• W. S. Lee, P. Bartlett, and R. C. Williamson, "Correction to "The Importance of Convexity in Learning with Squared Loss"," IEEE Trans. Information Theory, vol. 54, no. 9, pp. 4395-4395, Sep. 2008.
• M. Collins, A. Globerson, T. Koo, X. Carreras, and P. Bartlett, "Exponentiated gradient algorithms for conditional random fields and max-margine Markov networks," J. Machine Learning Research, vol. 9, no. 8, pp. 1775-1822, Aug. 2008.
• P. Bartlett and M. H. Wegkamp, "Classification with a reject option using a hinge loss," J. Machine Learning Research, vol. 9, no. 8, pp. 1823-1840, Aug. 2008.
• P. Bartlett and M. Traskin, "AdaBoost is consistent," J. Machine Learning Research, vol. 8, no. 10, pp. 2347-2368, Oct. 2007.
• 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.
• 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, 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

• V. Muthukumar, M. Ray, A. Sahai, and P. Bartlett, "Best of many worlds: Robust model selection for online supervised learning," in The 22nd International Conference on Artificial Intelligence and Statistics, 2019, pp. 3177--3186.
• 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.
• F. Hedayati and P. Bartlett, "Exchangeability Characterizes Optimality of SequentialNormalized Maximum Likelihood and Bayesian Prediction with JeffreysPrior," in Proceedings of The Fifteenth International Conference on Artificial Intelligence and Statistics(AISTATS), M. Girolami and N. Lawrence, Eds., 2012.
• J. Abernethy, P. Bartlett, N. Buchbinder, and I. Stanton, "A Regularization Approach to Metrical Task Systems," in Algorithmic Learning Theory, 21th International Conference, {ALT} 2010, Canberra, Australia, October 6-8, 2010, Proceedings, M. Hutter, F. Stephan, V. Vovk, and T. Zeugmann, Eds., Lecture Notes in Artificial Intelligence, Vol. 6331, Berlin, Heidelberg, New York: Springer, 2010, pp. 270--284.
• 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.
• M. Barreno, P. Bartlett, F. J. Chi, A. D. Joseph, B. Nelson, B. I. P. Rubinstein, U. Saini, and D. Tygar, "Open problems in the security of learning (Position Paper)," in Proc. 1st ACM Workshop on AISec (AISec 2008), New York, NY: The Association for Computing Machinery, Inc., 2008, pp. 19-26.
• 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

• M. Kloft, U. Rückert, and P. Bartlett, "A Unifying View of Multiple Kernel Learning," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2010-49, May 2010.
• A. Agarwal, A. Rakhlin, and P. Bartlett, "Matrix regularization techniques for online multitask learning," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2008-138, Oct. 2008.
• 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.
• P. Bartlett, "Fast Rates for Estimation Error and Oracle Inequalities for Model Selection," University of California, Department of Statistics, Tech. Rep. UCB/STAT-03-728, March 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.
• P. Bartlett and M. Traskin, "AdaBoost Is Consistent," University of California, Department of Statistics, Tech. Rep. UCB/STAT-12-722, Dec. 2006.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "Convexity, Classification, and Risk Bounds," University of California, Department of Statistics, Tech. Rep. UCB/STAT-04-638, April 2003.
• 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.

Masters Reports

• S. Rao and J. Hong, "Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications," P. Bartlett, Ed., EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2010-63, May 2010.