Peter Bartlett's 2006-2012 Papers

[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 ]
[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 ]
[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. [ 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 ]
[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 ]
[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 ]
[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. [ bib | .pdf ]
[RB07] 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, volume 2, pages 396-403, 2007. [ 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 ]
[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), pages 767-774, 2007. [ 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 ]
[ABRT07] Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, and Ambuj Tewari. Minimax lower bounds for online convex games. Technical report, UC Berkeley, 2007. [ 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 ]
[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 ]
[Bar08] Peter L. Bartlett. Fast rates for estimation error and oracle inequalities for model selection. Econometric Theory, 24(2):545-552, April 2008. (Was Department of Statistics, U.C. Berkeley Technical Report number 729, 2007). [ bib | DOI | .pdf | Abstract ]
[BW08] Peter L. Bartlett and Marten H. Wegkamp. Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 9:1823-1840, August 2008. [ bib | .pdf | Abstract ]
[BHR08] Peter L. Bartlett, Elad Hazan, and Alexander Rakhlin. Adaptive online gradient descent. In John Platt, Daphne Koller, Yoram Singer, and Sam Roweis, editors, Advances in Neural Information Processing Systems 20, pages 65-72, Cambridge, MA, September 2008. MIT Press. [ bib | .pdf | Abstract ]
[TB08] Ambuj Tewari and Peter L. Bartlett. Optimistic linear programming gives logarithmic regret for irreducible MDPs. In John Platt, Daphne Koller, Yoram Singer, and Sam Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1505-1512, Cambridge, MA, September 2008. MIT Press. [ 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, 9:1775-1822, August 2008. [ 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), pages 335-342, December 2008. [ 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), pages 415-423, December 2008. [ bib | .pdf ]
[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, 54(9):4395, September 2008. [ bib | .pdf ]
[NABH08a] Massieh Najafi, David M. Auslander, Peter L. Bartlett, and Philip Haves. Application of machine learning in fault diagnostics of mechanical systems. In Proceedings of the World Congress on Engineering and Computer Science 2008: International Conference on Modeling, Simulation and Control 2008, pages 957-962, October 2008. Winner of Best Student Paper Award of the Conference. [ bib | .pdf ]
[NABH08b] Massieh Najafi, David M. Auslander, Peter L. Bartlett, and Philip Haves. Fault diagnostics and supervised testing: How fault diagnostic tools can be proactive? In K. Grigoriadis, editor, Proceedings of Intelligent Systems and Control (ISC 2008), pages 633-034, September 2008. [ bib ]
[NABH08c] Massieh Najafi, David M. Auslander, Peter L. Bartlett, and Philip Haves. Overcoming the complexity of diagnostic problems due to sensor network architecture. In K. Grigoriadis, editor, Proceedings of Intelligent Systems and Control (ISC 2008), pages 633-071, September 2008. [ bib ]
[ARB08] Alekh Agarwal, Alexander Rakhlin, and Peter Bartlett. Matrix regularization techniques for online multitask learning. Technical Report UCB/EECS-2008-138, EECS Department, University of California, Berkeley, 2008. [ bib | .pdf | Abstract ]
[BBC+08] Marco Barreno, Peter L. Bartlett, F. J. Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, U. Saini, and J. Doug Tygar. Open problems in the security of learning. In Proceedings of the 1st ACM Workshop on AISec (AISec2008), pages 19-26, October 2008. [ bib | DOI ]
[RBR09] 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, 75(1):37-59, January 2009. (Was University of California, Berkeley, EECS Department Technical Report EECS-2007-86). [ bib | .pdf ]
[BT09] Peter L. Bartlett and Ambuj Tewari. REGAL: A regularization based algorithm for reinforcement learning in weakly communicating MDPs. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI2009), pages 35-42, June 2009. [ bib | .pdf ]
[AABR09a] Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, and Alexander Rakhlin. A stochastic view of optimal regret through minimax duality. In Proceedings of the 22nd Annual Conference on Learning Theory - COLT 2009, pages 257-266, June 2009. [ bib | .pdf | Abstract ]
[AABR09b] Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, and Alexander Rakhlin. A stochastic view of optimal regret through minimax duality. Technical Report 0903.5328, arxiv.org, 2009. [ bib | http | Abstract ]
[RSBN09] David S. Rosenberg, Vikas Sindhwani, Peter L. Bartlett, and Partha Niyogi. Multiview point cloud kernels for semisupervised learning. IEEE Signal Processing Magazine, 26(5):145-150, September 2009. [ bib | DOI ]
[ABRW09] Alekh Agarwal, Peter L. Bartlett, Pradeep Ravikumar, and Martin Wainwright. Information-theoretic lower bounds on the oracle complexity of convex optimization. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 1-9, June 2009. [ bib | .pdf | Abstract ]
[BRS+09] A. Barth, Benjamin I. P. Rubinstein, M. Sundararajan, J. C. Mitchell, Dawn Song, and Peter L. Bartlett. A learning-based approach to reactive security. Technical Report 0912.1155, arxiv.org, 2009. [ bib | http | Abstract ]
[BRS+10] A. Barth, Benjamin I. P. Rubinstein, M. Sundararajan, J. C. Mitchell, Dawn Song, and Peter L. Bartlett. A learning-based approach to reactive security. In Proceedings of Financial Cryptography and Data Security (FC10), pages 192-206, 2010. [ bib | DOI ]
[ABD10] Alekh Agarwal, Peter L. Bartlett, and Max Dama. Optimal allocation strategies for the dark pool problem. In Y. W. Teh and M. Titterington, editors, Proceedings of The Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), volume 9, pages 9-16, May 2010. [ bib | .pdf | Abstract ]
[Bar10a] Peter L. Bartlett. Learning to act in uncertain environments. Communications of the ACM, 53(5):98, May 2010. [ bib | DOI ]
[BMP10] Peter L. Bartlett, Shahar Mendelson, and Petra Philips. On the optimality of sample-based estimates of the expectation of the empirical minimizer. ESAIM: Probability and Statistics, 14:315-337, January 2010. [ bib | .pdf | Abstract ]
[ABBS10] Jacob Abernethy, Peter L. Bartlett, Niv Buchbinder, and Isabelle Stanton. A regularization approach to metrical task systems. In Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann, editors, Algorithmic Learning Theory, 21st International Conference, ALT 2010, pages 270-284, October 2010. [ bib | DOI ]
[Bar10b] Peter L. Bartlett. Optimal online prediction in adversarial environments. In Marcus Hutter, Frank Stephan, Vladimir Vovk, and Thomas Zeugmann, editors, Algorithmic Learning Theory, 21st International Conference, ALT 2010, page 34, October 2010. [ bib | DOI ]
[KRB10a] Marius Kloft, Ulrich Rückert, and Peter L. Bartlett. A unifying view of multiple kernel learning. In José L. Balcázar, Francesco Bonchi, Aristides Gionis, and Michèle Sebag, editors, Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD, pages 66-81, September 2010. Part II, LNAI 6322. [ bib | DOI ]
[KRB10b] Marius Kloft, Ulrich Rückert, and Peter L. Bartlett. A unifying view of multiple kernel learning. Technical Report 1005.0437, arxiv.org, 2010. [ bib | http | Abstract ]
[KB10] Brian Kulis and Peter L. Bartlett. Implicit online learning. In Johannes F├╝rnkranz and Thorsten Joachims, editors, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 575-582, June 2010. [ bib | .pdf ]
[RBR10] Benjamin I. P. Rubinstein, Peter L. Bartlett, and J. Hyam Rubinstein. Corrigendum to `shifting: One-inclusion mistake bounds and sample compression' [j. comput. system sci 75 (1) (2009) 37-59]. Journal of Computer and System Sciences, 76(3-4):278-280, May 2010. [ bib | DOI ]
[ABH10] Jacob Abernethy, Peter L. Bartlett, and Elad Hazan. Blackwell approachability and no-regret learning are equivalent. Technical Report 1011.1936, arxiv.org, 2010. [ bib | http | Abstract ]
[AB11] Sylvain Arlot and Peter L. Bartlett. Margin-adaptive model selection in statistical learning. Bernoulli, 17(2):687-713, May 2011. [ bib | .pdf ]
[RAB11] Afshin Rostamizadeh, Alekh Agarwal, and Peter L. Bartlett. Learning with missing features. In Avi Pfeffer and Fabio G. Cozman, editors, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI2011), pages 635-642, July 2011. [ bib | .pdf ]
[ABH11] Jacob Abernethy, Peter L. Bartlett, and Elad Hazan. Blackwell approachability and no-regret learning are equivalent. In Sham Kakade and Ulrike von Luxburg, editors, Proceedings of the Conference on Learning Theory (COLT2011), volume 19, pages 27-46, July 2011. [ bib | .pdf ]
[ADBL11] Alekh Agarwal, John Duchi, Peter L. Bartlett, and Clement Levrard. Oracle inequalities for computationally budgeted model selection. In Sham Kakade and Ulrike von Luxburg, editors, Proceedings of the Conference on Learning Theory (COLT2011), volume 19, pages 69-86, July 2011. [ bib | .pdf | Abstract ]
[RBHT12] Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft. Learning in a large function space: Privacy preserving mechanisms for SVM learning. Journal of Privacy and Confidentiality, 4(1):65-100, August 2012. [ bib | http ]
[NAB+12] Massieh Najafi, David M. Auslander, Peter L. Bartlett, Philip Haves, and Michael D. Sohn. Application of machine learning in the fault diagnostics of air handling units. Applied Energy, 96:347-358, August 2012. [ bib | DOI ]
[HB12b] Fares Hedayati and Peter L. Bartlett. Exchangeability characterizes optimality of sequential normalized maximum likelihood and Bayesian prediction with Jeffreys prior. In M. Girolami and N. Lawrence, editors, Proceedings of The Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), April 2012. To appear. [ bib | .pdf | Abstract ]
[BMN12] Peter L. Bartlett, Shahar Mendelson, and Joseph Neeman. l1-regularized linear regression: Persistence and oracle inequalities. Probability Theory and Related Fields, 154(1-2):193-224, October 2012. [ bib | DOI | .pdf | Abstract ]
[ABRW12] Alekh Agarwal, Peter Bartlett, Pradeep Ravikumar, and Martin Wainwright. Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization. IEEE Transactions on Information Theory, 58(5):3235-3249, May 2012. [ bib | DOI | Abstract ]
[BRS+12] A. Barth, Benjamin I. P. Rubinstein, M. Sundararajan, J. C. Mitchell, Dawn Song, and Peter L. Bartlett. A learning-based approach to reactive security. IEEE Transactions on Dependable and Secure Computing, 9(4):482-493, July 2012. [ bib | http ]
[STZB+11] John Shawe-Taylor, Richard Zemel, Peter L. Bartlett, Fernando Pereira, and Kilian Weinberger, editors. Advances in Neural Information Processing Systems 24. Proceedings of the 2011 Conference. NIPS Foundation, December 2011. [ bib | .html ]
[HB12a] Fares Hedayati and Peter Bartlett. The optimality of Jeffreys prior for online density estimation and the asymptotic normality of maximum likelihood estimators. In Proceedings of the Conference on Learning Theory (COLT2012), volume 23, pages 7.1-7.13, June 2012. [ bib | .pdf | Abstract ]
[DBW12] John Duchi, Peter L. Bartlett, and Martin J. Wainwright. Randomized smoothing for stochastic optimization. SIAM Journal on Optimization, 22(2):674-701, June 2012. [ bib | .pdf | Abstract ]
[TB13] Ambuj Tewari and Peter L. Bartlett. Learning theory. In E-Reference - Signal Processing. Elsevier, 2013. Chapter 27. To appear. [ bib | Abstract ]
[ABD12] Alekh Agarwal, Peter L. Bartlett, and John Duchi. Oracle inequalities for computationally adaptive model selection. Technical Report 1208.0129, arxiv.org, 2012. [ bib | http | Abstract ]

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