| [BJM06b] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473):138-156, 2006. (Was Department of Statistics, U.C. Berkeley Technical Report number 638, 2003). [ bib | .ps.gz | .pdf | Abstract ] |
| [BM06b] | Peter L. Bartlett and Shahar Mendelson. Empirical minimization. Probability Theory and Related Fields, 135(3):311-334, 2006. [ bib | .ps.gz | .pdf | Abstract ] |
| [BMP07] | Peter L. Bartlett, Shahar Mendelson, and Petra Philips. Optimal sample-based estimates of the expectation of the empirical minimizer. Technical report, U.C. Berkeley, 2007. [ bib | .ps.gz | .pdf | Abstract ] |
| [BMP08] | Peter L. Bartlett, Shahar Mendelson, and Petra Philips. Optimal sample-based estimates of the expectation of the empirical minimizer. ESAIM: Probability and Statistics, 2008. (Accepted). [ bib | .ps.gz | .pdf | Abstract ] |
| [Bar07] | Peter L. Bartlett. Fast rates for estimation error and oracle inequalities for model selection. Technical Report 729, Department of Statistics, U.C. Berkeley, 2007. [ bib | .pdf | Abstract ] |
| [Bar08] | Peter L. Bartlett. Fast rates for estimation error and oracle inequalities for model selection. Econometric Theory, 24(2), 2008. (To appear. Was Department of Statistics, U.C. Berkeley Technical Report number 729, 2007). [ bib | .pdf | Abstract ] |
| [BW06] | Peter L. Bartlett and Marten H. Wegkamp. Classification with a reject option using a hinge loss. Technical report, U.C. Berkeley, 2006. [ bib | .ps.gz | .pdf | Abstract ] |
| [BW08] | Peter L. Bartlett and Marten H. Wegkamp. Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 2008. (To appear.). [ bib | .ps.gz | .pdf | Abstract ] |
| [BT07a] | Peter L. Bartlett and Ambuj Tewari. Sample complexity of policy search with known dynamics. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 97-104, Cambridge, MA, 2007. MIT Press. [ bib | .pdf ] |
| [RBR07b] | Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein. Shifting, one-inclusion mistake bounds and tight multiclass expected risk bounds. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 1193-1200, Cambridge, MA, 2007. MIT Press. To appear. [ bib | .pdf ] |
| [RB07b] | David Rosenberg and Peter L. Bartlett. The Rademacher complexity of co-regularized kernel classes. In Marina Meila and Xiaotong Shen, editors, Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007. To appear. [ bib | .pdf | Abstract ] |
| [BT07c] | Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 105-112, Cambridge, MA, 2007. MIT Press. [ bib | .pdf | Abstract ] |
| [BT06b] | Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Technical report, U. C. Berkeley, 2006. [ bib ] |
| [BJM06a] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Comment. Statistical Science, 21(3):341-346, 2006. [ bib ] |
| [BT06a] | Peter L. Bartlett and Mikhail Traskin. Adaboost and other large margin classifiers: Convexity in pattern classification. In Proceedings of the 5th Workshop on Defence Applications of Signal Processing, 2006. To appear. [ bib ] |
| [BM06a] | Peter L. Bartlett and Shahar Mendelson. Discussion of “2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization” by V. Koltchinskii. The Annals of Statistics, 34(6):2657-2663, 2006. [ bib ] |
| [TB07a] | Ambuj Tewari and Peter L. Bartlett. Bounded parameter Markov decision processes with average reward criterion. In Proceedings of the Conference on Learning Theory, pages 263-277, 2007. [ bib ] |
| [ABR07a] | Jacob Abernethy, Peter L. Bartlett, and Alexander Rakhlin. Multitask learning with expert advice. In Proceedings of the Conference on Learning Theory, pages 484-498, 2007. [ bib | Abstract ] |
| [RAB07] | Alexander Rakhlin, Jacob Abernethy, and Peter L. Bartlett. Online discovery of similarity mappings. In Proceedings of the 24th International Conference on Machine Learning (ICML-2007), 2007. To appear. [ bib | Abstract ] |
| [BT07d] | Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Journal of Machine Learning Research, 8:2347-2368, 2007. [ bib | .pdf | Abstract ] |
| [RBR07a] | Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein. Shifting: one-inclusion mistake bounds and sample compression. Technical report, EECS Department, University of California, Berkeley, 2007. [ bib | .pdf | Abstract ] |
| [RBR08] | Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein. Shifting: one-inclusion mistake bounds and sample compression. Journal of Computer and System Sciences, 2008. (To appear. Was University of California, Berkeley, EECS Department Technical Report EECS-2007-86). [ bib | .pdf ] |
| [RB07a] | David Rosenberg and Peter L. Bartlett. On bounds for Bayesian sequence prediction with non-Gaussian priors. Technical Report, 2007. [ bib | Abstract ] |
| [ABRT07] | Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, and Ambuj Tewari. Minimax lower bounds for online convex games. Technical Report, 2007. [ bib | .pdf | Abstract ] |
| [BHR08] | Peter L. Bartlett, Elad Hazan, and Alexander Rakhlin. Adaptive online gradient descent. In Daphne Koller, Yoram Singer, and John Platt, editors, Advances in Neural Information Processing Systems 20, Cambridge, MA, 2008. MIT Press. To appear. [ bib | .pdf | Abstract ] |
| [TB08] | Ambuj Tewari and Peter L. Bartlett. Optimistic linear programming gives logarithmic regret for irreducible mdps. In Daphne Koller, Yoram Singer, and John Platt, editors, Advances in Neural Information Processing Systems 20, Cambridge, MA, 2008. MIT Press. To appear. [ bib | .pdf | Abstract ] |
| [ABR07b] | Jacob Duncan Abernethy, Peter L. Bartlett, and Alexander Rakhlin. Multitask learning with expert advice. Technical Report UCB/EECS-2007-20, EECS Department, University of California, Berkeley, 2007. [ bib | .html ] |
| [CGK+07] | Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, and Peter L. Bartlett. Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks. Technical report, U.C. Berkeley, 2007. [ bib | .pdf | Abstract ] |
| [CGK+08] | Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, and Peter L. Bartlett. Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks. Journal of Machine Learning Research, 2008. (Accepted). [ bib | .pdf | Abstract ] |
| [BDH+08] | Peter L. Bartlett, Varsha Dani, Thomas Hayes, Sham Kakade, Alexander Rakhlin, and Ambuj Tewari. High-probability regret bounds for bandit online linear optimization. In Proceedings of the 21st Annual Conference on Learning Theory (COLT 2008), 2008. (To appear). [ bib | .pdf ] |
| [ABRT08] | Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, and Ambuj Tewari. Optimal strategies and minimax lower bounds for online convex games. In Proceedings of the 21st Annual Conference on Learning Theory (COLT 2008), 2008. (To appear). [ bib | .pdf ] |
| [TB07b] | Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8:1007-1025, May 2007. (Invited paper). [ bib | .html ] |
| [BT07b] | Peter L. Bartlett and Ambuj Tewari. Sparseness vs estimating conditional probabilities: Some asymptotic results. Journal of Machine Learning Research, 8:775-790, April 2007. [ bib | .html ] |
| [LBW08] | Wee Sun Lee, Peter L. Bartlett, and Robert C. Williamson. Correction to the importance of convexity in learning with squared loss. IEEE Transactions on Information Theory, 2008. (To appear). [ bib | .pdf ] |
| [AB08] | Sylvain Arlot and Peter L. Bartlett. Margin-adaptive model selection in statistical learning. Submitted, 2008. [ bib | .pdf ] |
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