Browse By Person: Bartlett, Peter
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Number of items: 58.
1998
Schapire, R. E., Freund, Y., Bartlett, P.L., & Lee, W. S. (1998) Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5), pp. 1651-1686.
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707 470Bartlett, P.L. (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2), pp. 525-536.
518 234 222
518 234 2221999
Anthony, Martin & Bartlett, P. L. (1999) Neural network learning:theoretical foundations. Cambridge University Press, Cambridge, U.K..
2001
Baxter, J. & Bartlett, P. L. (2001) Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Research, 15, 319-350 .
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1492002
Lanckriet, Gert R. G., Cristianini, Nello, Bartlett, Peter, El Ghaoui, Laurent, & Jordan, Michael I. (2002) Learning the Kernel Matrix with Semi-Definite Programming. University of California, Berkeley, California (USA).
Bartlett, P. L., Boucheron, S., & Lugosi, G. (2002) Model selection and error estimation. Machine Learning, 48(1-3), pp. 85-113.
101 85
101 852003
Bartlett, P. L. & Mendelson, S. (2003) Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3(3), pp. 463-482.
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2012004
Lanckriet, G. R. G., Cristianini, N., Bartlett, P. L., El Ghaoui, L., & Jordan, M. I. (2004) Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5, pp. 27-72.
757 468
757 4682005
Bartlett, Peter L., Collins, Michael J., Taskar, Ben, & McAllester, David A. (2005) Exponentiated gradient algorithms for large-margin structured classification. In Saul , Lawrence K., Weiss, Yair, & Bottou, Léon (Eds.) Advances in Neural Information Processing Systems 17 : Proceedings of the 2004 Conference, MIT Press, Hyatt Regency, Vancouver, pp. 113-120.
Bartlett, Peter L., Bousquet, Olivier, & Mendelson, Shahar (2005) Local Rademacher complexities. The Annals of Statistics, 33(4), pp. 1497-1537.
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72 63Tewari, Ambuj & Bartlett, Peter L. (2005) On the Consistency of Multiclass Classification Methods. Learning Theory, 3559, pp. 272-284.
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6Tewari, Ambuj & Bartlett, Peter L. (2005) On the consistency of multiclass classification methods. Lecture Notes in Computer Science, 3559, pp. 143-157.
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62006
Bartlett, Peter L. & Mendelson, Shahar (2006) Empirical minimization. Probability Theory and Related Fields, 135(3), pp. 311-334.
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24 22Bartlett, Peter L. & Traskin, Mikhail (2006) Adaboost and other large margin classifiers : convexity in pattern classification. In Proceedings of the 2006 Workshop on Defense Applications of Signal Processing (DASP'06), The Kingfisher Bay Resort, Fraser Island, Queensland, Australia.
Bartlett, Peter L., Jordan, Michael I., & McAuliffe, Jon D. (2006) Comment. Statistical Science, 21(3), pp. 341-346.
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2Bartlett, Peter L., Jordan, Michael I, & McAuliffe, Jon D (2006) Convexity, Classification, and Risk Bounds. Journal of the American Statistical Association, 101(473), pp. 138-156.
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170 144Bartlett, Peter L. & Mendelson, Shahar (2006) Discussion : local Rademacher complexities and oracle inequalities in risk minimization. The Annals of Statistics, 34(6), pp. 2657-2663.
Bartlett, Peter & Mendelson, Shahar (2006) Discussion: Local Rademacher complexities and oracle inequalities in risk minimization. Annals of Statistics, 34(6), pp. 2657-2663.
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1Jimenez-Rodriquez , R., Sitar, N., & Bartlett, P.L. (2006) Maximum likelihood estimation of trace length distribution parameters using the EM algorithm. In Barla, G. & Barla, M. (Eds.) Prediction, Analysis and Design in Geomechanical Applications : Proceedings of the Eleventh International Conference on Computer Methods and Advances in Geomechanics (IACMAG-2005), Pàtron editore S.r.l., Torino, Italy, pp. 619-626.
2007
Bartlett, Peter L. & Traskin, Mikhail (2007) Adaboost is consistent. Journal of Machine Learning Research, 8, pp. 2347-2368.
385 22 14
385 22 14Rubinstein, Benjamin I. P., Bartlett, Peter L., & Rubinstein, J. Hyamm (2007) Shifting, one-inclusion mistake bounds and tight multiclass expected risk bounds. In Schölkopf, Bernhard, Platt, John, & Hofmann, Thomas (Eds.) Advances in Neural Information Processing Systems 19 : Proceedings of the 2006 Conference, MIT Press, Hyatt Regency, Vancouver, pp. 1193-1208.
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14 3Rakhlin, Alexander, Tewari, Ambuj, & Bartlett, Peter L. (2007) Closing the gap between bandit and full-information online optimization : high-probability regret bound. University of California, Berkeley, California (USA).
Bartlett, Peter L., Hazan, Elad, & Rakhlin, Alexander (2007) Adaptive online gradient descent. University of California, Berkeley, California.
Tewari, Ambuj & Bartlett, Peter L. (2007) On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8, pp. 1007-1025.
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30 19Bartlett, Peter L. & Tewari, Ambuj (2007) Sparseness vs estimating conditional probabilities : some asymptotic results. Journal of Machine Learning Research, 8(April), pp. 775-790.
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19 14Abernethy, Jacob D., Bartlett, Peter L., & Rakhlin, Alexander (2007) Multitask learning with expert advice. University of California, Berkeley, California.
Tewari, Ambuj & Bartlett, Peter L. (2007) Bounded parameter Markov Decision Processes with average reward criterion. Learning Theory, 4539, pp. 263-277.
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3Abernethy, Jacob, Bartlett, Peter L., & Rakhlin, Alexander (2007) Multitask learning with expert advice. Learning Theory, 4539, pp. 484-498.
Rakhlin, Alexander, Abernethy, Jacob, & Bartlett, Peter L. (2007) Online discovery of similarity mappings. In Proceedings of the 24th international conference on Machine learning - ICML '07, Association for Computing Machinery, Oregon State University in Corvallis, Oregon, pp. 767-774.
Rubinstein, Benjamin I.P., Bartlett, Peter L., & Rubinstein, J. Hyam (2007) Shifting : one-inclusion mistake bounds and sample compression. University of California, Berkeley, Berkeley, California.
Rosenberg, David & Bartlett, Peter L. (2007) The rademacher complexity of coregularized kernel classes. In Meila, Marina & Shen, Xiaotng (Eds.) 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007), 21- 24 March 2007, Caribe Hilton Hotel, San Juan, Puerto Rico.
2008
Lee, Wee Sun, Bartlett, Peter L., & Williamson, Robert C. (2008) Correction to “The Importance of Convexity in Learning With Squared Loss” [Sep 98 1974-1980]. IEEE Transactions on Information Theory, 54(9), p. 4395.
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31Bartlett, Peter L. & Wegkamp, Marten H. (2008) Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 9, pp. 1823-1840.
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33 18Collins, Michael, Globerson, Amir, Koo, Terry, Carreras, Xavier, & Bartlett, Peter L. (2008) Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks. Journal of Machine Learning Research, 9(Aug), pp. 1775-1822.
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40 18Bartlett, Peter L., Dani, Varsha, Hayes, Thomas, Kakade, Sham, Rakhlin, Alexander, & Tewari, Ambuj (2008) High-probability regret bounds for bandit online linear optimization. In 21th Annual Conference on Learning Theory (COLT 2008), 9-12 July 2008, Helsinki, Finland.
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35Abernethy, Jacob D., Bartlett, Peter L., Rakhlin, Alexander, & Tewari, Ambuj (2008) Optimal strategies and minimax lower bounds for online convex games. University of California, Berkeley, Berkeley, California (USA).
Najafi, Massieh, Auslander, David M., Bartlett, Peter L., & Haves, Philip (2008) 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, International Association of Engineers, San Fransisco, pp. 957-962.
Bartlett, Peter L. (2008) Fast rates for estimation error and oracle inequalities for model section. Econometric Theory, 24(2), pp. 545-552.
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5 7Najafi, M., Auslander, D.M., Bartlett, P.L., & Haves, P. (2008) Fault diagnostics and supervised testing : How fault diagnostic tools can be proactive? In Grigoriadis , K. (Ed.) Proceeding (633) Intelligent Systems and Control - 2008, ACTA Press, Orlando, USA , pp. 34-45.
Barreno, M., Bartlett, P.L., Chi, F.J., Joseph, A.D., Nelson, B., Rubinstein, B.I.P., et al. (2008) Open problems in the security of learning. In Proceedings of the 1st ACM workshop on Workshop on AISec - AISec '08, Association for Computing Machinery, Alexandria, VA, pp. 19-26.
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7Tewari, Ambuj & Bartlett, Peter L. (2008) Optimistic linear programming gives logarithmic regret for irreducible MDPs. In Platt, John, Koller, Daphne, Singer, Yoram, & Rowies, Sam (Eds.) Advances in Neural Information Processing Systems 20 (NIPS), 2008, Cambridge, MA.
Najafi, M., Auslander, D.M., Bartlett, P.L., & Haves, P. (2008) Overcoming the complexity of diagnostic problems due to sensor network architecture. In Grigoriadis , K. (Ed.) Proceeding (633) Intelligent Systems and Control - 2008, ACTA Press, Orlando, USA.
2009
Rosenberg, David, Sindhwani, Vikas, Bartlett, Peter L., & Niyogi, Partha (2009) Multiview point cloud kernels for semisupervised learning [Lecture Notes]. IEEE Signal Processing Magazine, 26(5), 145-150 .
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16 2 2Rubinstein, B., Bartlett, P.L., & Rubinstein, J. (2009) Shifting : one-inclusion mistake bounds and sample compression. Journal of Computer and System Sciences, 75(1), pp. 37-59.
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4 3Bartlett, Peter L. & Tewari, Ambuj (2009) REGAL : a regularization based algorithm for reinforcement learning in weakly communicating MDPs. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)), McGill University, Montreal.
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2Abernethy, Jacob, Agarwal, Alekh, & Bartlett, Peter L. (2009) A Stochastic View of Optimal Regret through Minimax Duality. In Dasgupta , S & Klivans, A (Eds.) Proceedings of the 22nd Annual Conference on Learning Theory, Montreal, Quebec.
15
152010
Kloft, Marius, Rückert, Ulrich, & Bartlett, Peter L. (2010) A unifying view of multiple kernel learning. University of California, Berkeley.
Bartlett, Peter L. (2010) Learning to act in uncertain environments : technical perspective. Communications of the ACM, 53(5), p. 98.
Bartlett, Peter L., Mendelson, Shahar, & Philips, Petra (2010) On the optimality of sample-based estimates of the expectation of the empirical minimizer. ESAIM : Probability and Statistics, 14(Jan), pp. 315-337.
Rubinstein, Benjamin I.P., Bartlett, Peter L., & Rubinstein, J. Hyam (2010) 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), pp. 278-280.
Kulis, Brian & Bartlett, Peter L. (2010) Implicit online learning. In Proceedings of the 27 th International Conference on Machine Learning, Haifa, Israel.
Agarwal, Alekh, Bartlett, Peter L., & Dama, Max (2010) Optimal allocation strategies for the dark pool problem. In Teh, Yee Whye & Titterington, Mike (Eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy, pp. 9-16.
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20 1Bartlett, Peter L. (2010) Optimal online prediction in adversarial environments. Algorithmic Learning Theory, 6331, p. 34.
Bartlett, Peter L. (2010) Optimal online prediction in adversarial environments. Discovery Science, 6332, p. 371.
Abernethy, Jacob, Bartlett, Peter L., Buchbinder, Niv, & Stanton, Isabelle (2010) A Regularization Approach to Metrical Task Systems. LNCS- Algorithmic Learning Theory, 6331, pp. 270-284.
Barth, Adam, Rubinstein, Benjamin I.P., Sundararajan, Mukund, Mitchell, John C., Song, Dawn, & Bartlett, Peter L. (2010) A learning-based approach to reactive security. Financial Cryptography and Data Security, 6052, pp. 192-206.
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3 3Kloft, Marius, Rückert, Ulrich, & Bartlett, Peter L. (2010) A unifying view of multiple kernel learning. Machine Learning and Knowledge Discovery in Databases, 6322, pp. 66-81.
2011
Arlot, Sylvain & Bartlett, Peter L. (2011) Margin-adaptive model selection in statistical learning. Bernoulli.
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