Peter Bartlett's Publications (1993-2012)

[1] P. L. Bartlett. Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks. In Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, pages 144-150. ACM Press, 1993. [ bib ]
[2] P. L. Bartlett. The sample size necessary for learning in multi-layer networks. In Proceedings of the Fourth Australian Conference on Neural Networks, pages 14-17, 1993. [ bib ]
[3] D. R. Lovell and P. L. Bartlett. Error and variance bounds in multi-layer neural networks. In Proceedings of the Fourth Australian Conference on Neural Networks, pages 161-164, 1993. [ bib ]
[4] P. L. Bartlett. Vapnik-Chervonenkis dimension bounds for two- and three-layer networks. Neural Computation, 5(3):371-373, 1993. [ bib ]
[5] W. S. Lee, P. L. Bartlett, and R. C. Williamson. The Vapnik-Chervonenkis dimension of neural networks with restricted parameter ranges. In Proceedings of the Fifth Australian Conference on Neural Networks, pages 198-201, 1994. [ bib ]
[6] P. L. Bartlett. Learning quantized real-valued functions. In Proceedings of Computing: the Australian Theory Seminar, pages 24-35. University of Technology Sydney, 1994. [ bib ]
[7] W. S. Lee, P. L. Bartlett, and R. C. Williamson. Lower bounds on the VC-dimension of smoothly parametrized function classes. In Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, pages 362-367. ACM Press, 1994. [ bib ]
[8] P. L. Bartlett, P. M. Long, and R. C. Williamson. Fat-shattering and the learnability of real-valued functions. In Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, pages 299-310. ACM Press, 1994. [ bib ]
[9] P. L. Bartlett, P. Fischer, and K.-U. Höffgen. Exploiting random walks for learning. In Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, pages 318-327. ACM Press, 1994. [ bib ]
[10] P. L. Bartlett. Computational learning theory. In A. Kent and J. G. Williams, editors, Encyclopedia of Computer Science and Technology, volume 31, pages 83-99. Marcel Dekker, 1994. [ bib ]
[11] W. S. Lee, P. L. Bartlett, and R. C. Williamson. Efficient agnostic learning of neural networks with bounded fan-in. In Proceedings of the Sixth Australian Conference on Neural Networks, pages 201-204, 1995. [ bib ]
[12] P. L. Bartlett and R. C. Williamson. The sample complexity of neural network learning with discrete inputs. In Proceedings of the Sixth Australian Conference on Neural Networks, pages 189-192, 1995. [ bib ]
[13] M. Anthony and P. L. Bartlett. Function learning from interpolation. In Computational Learning Theory: Second European Conference, EUROCOLT 95, Barcelona Spain, March 1995, Proceedings, pages 211-221, 1995. [ bib ]
[14] W. S. Lee, P. L. Bartlett, and R. C. Williamson. On efficient agnostic learning of linear combinations of basis functions. In Proceedings of the Eighth Annual ACM Conference on Computational Learning Theory, pages 369-376. ACM Press, 1995. [ bib ]
[15] P. L. Bartlett and P. M. Long. More theorems about scale sensitive dimensions and learning. In Proceedings of the Eighth Annual ACM Conference on Computational Learning Theory, pages 392-401. ACM Press, 1995. [ bib ]
[16] P. L. Bartlett and S. Dasgupta. Exponential convergence of a gradient descent algorithm for a class of recurrent neural networks. In Proceedings of the 38th Midwest Symposium on Circuits and Systems, 1995. [ bib ]
[17] W. S. Lee, P. L. Bartlett, and R. C. Williamson. Lower bounds on the VC-dimension of smoothly parametrized function classes. Neural Computation, 7:990-1002, 1995. (See also correction, Neural Computation, 9: 765-769, 1997). [ bib ]
[18] L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Adaptive tracking identification: the art of defalsification. In Proceedings of the 1996 IFAC World Congress, 1996. [ bib ]
[19] W. S. Lee, P. L. Bartlett, and R. C. Williamson. The importance of convexity in learning with squared loss. In Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages 140-146. ACM Press, 1996. [ bib ]
[20] J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony. A framework for structural risk minimization. In Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages 68-76. ACM Press, 1996. [ bib ]
[21] P. L. Bartlett, S. Ben-David, and S. R. Kulkarni. Learning changing concepts by exploiting the structure of change. In Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages 131-139. ACM Press, 1996. [ bib ]
[22] L. Kammer, R. R. Bitmead, and P. L. Bartlett. Signal-based testing of LQ-optimality of controllers. In Proceedings of the 35th IEEE Conference on Decision and Control, pages FA17-2, 3620-3623. IEEE, 1996. [ bib ]
[23] P. L. Bartlett and S. R. Kulkarni. The complexity of model classes, and smoothing noisy data (invited). In Proceedings of the 35th IEEE Conference on Decision and Control, pages TM09-4, 2312-2317. IEEE, 1996. [ bib ]
[24] A. Kowalczyk, J. Szymanski, P. L. Bartlett, and R. C. Williamson. Examples of learning curves from a modified VC-formalism. In Advances in Neural Information Processing Systems 8, 1996. [ bib ]
[25] P. L. Bartlett and R. C. Williamson. The Vapnik-Chervonenkis dimension and pseudodimension of two-layer neural networks with discrete inputs. Neural Computation, 8:653-656, 1996. [ bib ]
[26] P. L. Bartlett, P. M. Long, and R. C. Williamson. Fat-shattering and the learnability of real-valued functions. Journal of Computer and System Sciences, 52(3):434-452, 1996. (special issue on COLT`94). [ bib ]
[27] M. Anthony, P. L. Bartlett, Y. Ishai, and J. Shawe-Taylor. Valid generalisation from approximate interpolation. Combinatorics, Probability, and Computing, 5:191-214, 1996. [ bib ]
[28] W. S. Lee, P. L. Bartlett, and R. C. Williamson. Efficient agnostic learning of neural networks with bounded fan-in. IEEE Transactions on Information Theory, 42(6):2118-2132, 1996. [ bib ]
[29] Peter L. Bartlett, Anthony Burkitt, and Robert C. Williamson. Proceedings of the Seventh Australian Conference on Neural Networks. Australian National University, 1996. [ bib ]
[30] G. Loy and P. L. Bartlett. Generalization and the size of the weights: an experimental study. In Proceedings of the Eighth Australian Conference on Neural Networks, pages 60-64, 1997. [ bib ]
[31] P. L. Bartlett. Neural network learning. (abstract of invited talk.). In CONTROL 97 Conference Proceedings, Institution of Engineers Australia, page 543, 1997. [ bib ]
[32] J. Baxter and P. L. Bartlett. A result relating convex n-widths to covering numbers with some applications to neural networks. In S. Ben-David, editor, Proceedings of the Third European Conference on Computational Learning Theory (EuroCOLT'97), pages 251-259. Springer, 1997. [ bib ]
[33] P. L. Bartlett, T. Linder, and G. Lugosi. A minimax lower bound for empirical quantizer design. In S. Ben-David, editor, Proceedings of the Third European Conference on Computational Learning Theory (EuroCOLT'97), pages 220-222. Springer, 1997. [ bib ]
[34] P. L. Bartlett, T. Linder, and G. Lugosi. The minimax distortion redundancy in empirical quantizer design (abstract). In Proceedings of the 1997 IEEE International Symposium on Information Theory, page 511, 1997. [ bib ]
[35] R. E. Schapire, Y. Freund, P. L. Bartlett, and W. S. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. In Machine Learning: Proceedings of the Fourteenth International Conference, pages 322-330, 1997. [ bib ]
[36] P. L. Bartlett. For valid generalization, the size of the weights is more important than the size of the network. In Advances in Neural Information Processing Systems 9, pages 134-140, 1997. [ bib ]
[37] W. S. Lee, P. L. Bartlett, and R. C. Williamson. Correction to `lower bounds on the VC-dimension of smoothly parametrized function classes'. Neural Computation, 9:765-769, 1997. [ bib ]
[38] P. L. Bartlett. Book review: `Neural networks for pattern recognition,' Christopher M. Bishop. Statistics in Medicine, 16(20):2385-2386, 1997. [ bib ]
[39] P. L. Bartlett, S. R. Kulkarni, and S. E. Posner. Covering numbers for real-valued function classes. IEEE Transactions on Information Theory, 43(5):1721-1724, 1997. [ bib ]
[40] L. Mason, P. L. Bartlett, and M. Golea. Generalization in threshold networks, combined decision trees and combined mask perceptrons. In T. Downs, M. Frean, and M. Gallagher, editors, Proceedings of the Ninth Australian Conference on Neural Networks (ACNN'98), pages 84-88. University of Queensland, 1998. [ bib ]
[41] B. Schölkopf, P. L. Bartlett, A. Smola, and R. Williamson. Support vector regression with automatic accuracy control. In L. Niklasson, M. Boden, and T. Ziemke, editors, Perspectives in Neural Computing: Proceedings of the 8th International Conference on Artificial Neural Networks (ICANN'98), pages 111-116. Springer-Verlag, 1998. [ bib ]
[42] L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Direct iterative tuning via spectral analysis. In Proceedings of the IEEE Conference on Decision and Control, volume 3, pages 2874-2879, 1998. [ bib ]
[43] M. Golea, P. L. Bartlett, and W. S. Lee. Generalization in decision trees and DNF: Does size matter? In Advances in Neural Information Processing Systems 10, pages 259-265, 1998. [ bib ]
[44] J. Baxter and P. L. Bartlett. The canonical distortion measure in feature space and 1-NN classification. In Advances in Neural Information Processing Systems 10, pages 245-251, 1998. [ bib ]
[45] P. L. 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 Transactions on Information Theory, 44(2):525-536, 1998. [ bib ]
[46] P. L. Bartlett and P. M. Long. Prediction, learning, uniform convergence, and scale-sensitive dimensions. Journal of Computer and System Sciences, 56(2):174-190, 1998. (special issue on COLT`95). [ bib ]
[47] L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Optimal controller properties from closed-loop experiments. Automatica, 34(1):83-91, 1998. [ bib ]
[48] P. L. Bartlett and M. Vidyasagar. Introduction to the special issue on learning theory. Systems and Control Letters, 34:113-114, 1998. [ bib ]
[49] P. L. Bartlett and S. Kulkarni. The complexity of model classes, and smoothing noisy data. Systems and Control Letters, 34(3):133-140, 1998. [ bib ]
[50] P. L. Bartlett, T. Linder, and G. Lugosi. The minimax distortion redundancy in empirical quantizer design. IEEE Transactions on Information Theory, 44(5):1802-1813, 1998. [ bib ]
[51] J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5):1926-1940, 1998. [ bib ]
[52] W. S. Lee, P. L. Bartlett, and R. C. Williamson. The importance of convexity in learning with squared loss. IEEE Transactions on Information Theory, 44(5):1974-1980, 1998. [ bib ]
[53] P. L. Bartlett, V. Maiorov, and R. Meir. Almost linear VC dimension bounds for piecewise polynomial networks. Neural Computation, 10(8):2159-2173, 1998. [ bib ]
[54] R. E. Schapire, Y. Freund, P. L. Bartlett, and W. S. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5):1651-1686, 1998. [ bib ]
[55] Peter L. Bartlett and Yishay Mansour, editors. Proceedings of the Eleventh Annual Conference on Computational Learning Theory. ACM Press, 1998. [ bib ]
[56] P. L. Bartlett and J. Baxter. Voting methods for data segmentation. In Proceedings of the Advanced Investment Technology Conference, pages 35-40. Bond University, 1999. [ bib ]
[57] L. Mason, P. L. Bartlett, and J. Baxter. Error bounds for voting classifiers using margin cost functions (invited abstract). In Proceedings of the IEEE Information Theory Workshop on Detection, Estimation, Classification and Imaging, page 36, 1999. [ bib ]
[58] P. L. Bartlett and S. Ben-David. Hardness results for neural network approximation problems. In Proceedings of the Fourth European Conference on Computational Learning Theory, pages 50-62, 1999. [ bib ]
[59] Y. Guo, P. L. Bartlett, J. Shawe-Taylor, and R. C. Williamson. Covering numbers for support vector machines. In Proceedings of the Twelfth Annual Conference on Computational Learning Theory, pages 267-277, 1999. [ bib ]
[60] T. Koshizen, P. L. Bartlett, and A. Zelinsky. Sensor fusion of odometry and sonar sensors by the Gaussian mixture Bayes' technique in mobile robot position estimation. In Proceedings of the 1999 IEEE International Conference on Systems, Man and Cybernetics, volume 4, pages 742-747, 1999. [ bib ]
[61] B. Schölkopf, P. L. Bartlett, A. Smola, and R. Williamson. Shrinking the tube: a new support vector regression algorithm. In Advances in Neural Information Processing Systems 11, pages 330-336, 1999. [ bib ]
[62] L. Mason, P. L. Bartlett, and J. Baxter. Direct optimization of margins improves generalization in combined classifiers. In Advances in Neural Information Processing Systems 11, pages 288-294, 1999. [ bib ]
[63] P. L. Bartlett, V. Maiorov, and R. Meir. Almost linear VC dimension bounds for piecewise polynomial networks. In Advances in Neural Information Processing Systems 11, pages 190-196, 1999. [ bib ]
[64] P. L. Bartlett and J. Shawe-Taylor. Generalization performance of support vector machines and other pattern classifiers. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 43-54. MIT Press, 1999. [ bib ]
[65] P. L. Bartlett. Efficient neural network learning. In V. D. Blondel, E. D. Sontag, M. Vidyasagar, and J. C. Willems, editors, Open Problems in Mathematical Systems Theory and Control, pages 35-38. Springer Verlag, 1999. [ bib ]
[66] P. L. Bartlett and G. Lugosi. An inequality for uniform deviations of sample averages from their means. Statistics and Probability Letters, 44(1):55-62, 1999. [ bib ]
[67] Martin Anthony and Peter L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, 1999. [ bib | .html ]
[68] P. L. Bartlett and J. Baxter. Estimation and approximation bounds for gradient-based reinforcement learning. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, pages 133-141, 2000. [ bib ]
[69] P. L. Bartlett, S. Boucheron, and G. Lugosi. Model selection and error estimation. In Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, pages 286-297, 2000. [ bib ]
[70] J. Baxter and P. L. Bartlett. GPOMDP: An on-line algorithm for estimating performance gradients in POMDP's, with applications. In Proceedings of the 2000 International Conference on Machine Learning, pages 41-48, 2000. [ bib ]
[71] L. Mason, J. Baxter, P. L. Bartlett, and M. Frean. Boosting algorithms as gradient descent. In Advances in Neural Information Processing Systems 12, pages 512-518, 2000. [ bib ]
[72] J. Baxter and P. L. Bartlett. Direct gradient-based reinforcement learning (invited). In Proceedings of the International Symposium on Circuits and Systems, pages III-271-274, 2000. [ bib ]
[73] P. L. Bartlett and J. Baxter. Stochastic optimization of controlled partially observable Markov decision processes. In Proceedings of the IEEE Conference on Decision and Control, volume 1, pages 124-129, 2000. [ bib ]
[74] L. Mason, P. L. Bartlett, and J. Baxter. Improved generalization through explicit optimization of margins. Machine Learning, 38(3):243-255, 2000. [ bib ]
[75] L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Direct iterative tuning via spectral analysis. Automatica, 36(9):1301-1307, 2000. [ bib ]
[76] B. Schölkopf, A. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12(5):1207-1245, 2000. [ bib ]
[77] S. Parameswaran, M. F. Parkinson, and P. L. Bartlett. Profiling in the ASP codesign environment. Journal of Systems Architecture, 46(14):1263-1274, 2000. [ bib ]
[78] P. L. Bartlett, S. Ben-David, and S. R. Kulkarni. Learning changing concepts by exploiting the structure of change. Machine Learning, 41(2):153-174, 2000. [ bib ]
[79] A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans. Introduction to large margin classifiers. In Advances in Large Margin Classifiers, pages 1-29. MIT Press, 2000. [ bib ]
[80] L. Mason, J. Baxter, P. L. Bartlett, and M. Frean. Functional gradient techniques for combining hypotheses. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 221-246. MIT Press, 2000. [ bib ]
[81] M. Anthony and P. L. Bartlett. Function learning from interpolation. Combinatorics, Probability, and Computing, 9:213-225, 2000. [ bib ]
[82] Alexander J. Smola, Peter L. Bartlett, Bernard Schölkopf, and Dale Schuurmans, editors. Advances in Large Margin Classifiers. MIT Press, 2000. [ bib ]
[83] A. J. Smola and P. L. Bartlett. Sparse greedy Gaussian process regression. In Advances in Neural Information Processing Systems 13, pages 619-625, 2001. [ bib ]
[84] P. L. Bartlett and S. Mendelson. Rademacher and Gaussian complexities: Risk bounds and structural results. In Proceedings of the Fourteenth Annual Conference on Computational Learning Theory and Fifth European Conference on Computational Learning Theory, pages 224-240, 2001. [ bib ]
[85] A. Ben-Hur, T. Barnes, P. L. Bartlett, O. Chapelle, A. Elisseeff, H. Fristche, I. Guyon, B. Schölkopf, J. Weston, E. Fung, C. Enderwick, E. A. Dalmasso, B.-L. Adam, J. W. Davis, A. Vlahou, L. Cazares, M. Ward, P. F. Schellhammer, J. Semmes, and G. L. Wright. Application of support vector machines to the classification of proteinchip system mass spectral data of prostate cancer serum samples (abstract). In Second Annual National Cancer Institute Early Detection Research Network Scientific Workshop, 2001. [ bib ]
[86] J. Baxter, P. L. Bartlett, and L. Weaver. Experiments with infinite-horizon, policy-gradient estimation. Journal of Artificial Intelligence Research, 15:351-381, 2001. [ bib | .html ]
[87] J. Baxter and P. L. Bartlett. Infinite-horizon gradient-based policy search. Journal of Artificial Intelligence Research, 15:319-350, 2001. [ bib | .html ]
[88] E. Greensmith, P. L. Bartlett, and J. Baxter. Variance reduction techniques for gradient estimates in reinforcement learning. In Advances in Neural Information Processing Systems 14, pages 1507-1514, 2002. [ bib | .ps.gz ]
[89] G. Lanckriet, N. Cristianini, P. L. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semi-definite programming. In Proceedings of the International Conference on Machine Learning, pages 323-330, 2002. [ bib ]
[90] P. L. Bartlett, O. Bousquet, and S. Mendelson. Localized Rademacher complexity. In Proceedings of the Conference on Computational Learning Theory, pages 44-58, 2002. [ bib ]
[91] L. Mason, P. L. Bartlett, and M. Golea. Generalization error of combined classifiers. Journal of Computer and System Sciences, 65(2):415-438, 2002. [ bib | http ]
[92] P. L. Bartlett, P. Fischer, and K.-U. Höffgen. Exploiting random walks for learning. Information and Computation, 176(2):121-135, 2002. [ bib | http ]
[93] P. L. Bartlett and S. Ben-David. Hardness results for neural network approximation problems. Theoretical Computer Science, 284(1):53-66, 2002. (special issue on Eurocolt'99). [ bib | http ]
[94] P. L. Bartlett and J. Baxter. Estimation and approximation bounds for gradient-based reinforcement learning. Journal of Computer and System Sciences, 64(1):133-150, 2002. [ bib ]
[95] P. L. Bartlett, S. Boucheron, and G. Lugosi. Model selection and error estimation. Machine Learning, 48:85-113, 2002. [ bib | .ps.gz ]
[96] P. L. Bartlett and S. Mendelson. Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3:463-482, 2002. [ bib | .pdf ]
[97] Y. Guo, P. L. Bartlett, J. Shawe-Taylor, and R. C. Williamson. Covering numbers for support vector machines. IEEE Transactions on Information Theory, 48(1):239-250, 2002. [ bib ]
[98] Peter L. Bartlett. An introduction to reinforcement learning theory: value function methods. In Shahar Mendelson and Alexander J. Smola, editors, Advanced Lectures on Machine Learning, volume 2600, pages 184-202. Springer, 2003. [ bib ]
[99] Peter L. Bartlett and Wolfgang Maass. Vapnik-Chervonenkis dimension of neural nets. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 1188-1192. MIT Press, 2003. Second Edition. [ bib | .ps.gz | .pdf ]
[100] Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Convexity, classification, and risk bounds. Technical Report 638, Department of Statistics, U.C. Berkeley, 2003. [ bib | .ps.Z | .pdf | Abstract ]
[101] Peter L. Bartlett. Prediction algorithms: complexity, concentration and convexity. In Proceedings of the 13th IFAC Symposium on System Identification, pages 1507-1517, 2003. [ bib | .ps.Z | Abstract ]
[102] Peter L. Bartlett, Shahar Mendelson, and Petra Philips. Local complexities for empirical risk minimization. In Proceedings of the 17th Annual Conference on Computational Learning Theory (COLT2004), volume 3120, pages 270-284. Springer, 2004. [ bib | .ps.gz | .pdf | Abstract ]
[103] Peter L. Bartlett and Ambuj Tewari. Sparseness vs estimating conditional probabilities: Some asymptotic results. In Proceedings of the 17th Annual Conference on Learning Theory, volume 3120, pages 564-578. Springer, 2004. [ bib | .ps.gz | .pdf | Abstract ]
[104] Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Large margin classifiers: convex loss, low noise, and convergence rates. In Advances in Neural Information Processing Systems, 16, 2004. [ bib | .ps.gz | Abstract ]
[105] Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Discussion of boosting papers. The Annals of Statistics, 32(1):85-91, 2004. [ bib | .ps.Z | .pdf ]
[106] E. Greensmith, P. L. Bartlett, and J. Baxter. Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5:1471-1530, 2004. [ bib | .pdf ]
[107] G. Lanckriet, N. Cristianini, P. L. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research, 5:27-72, 2004. [ bib | .ps.gz | .pdf ]
[108] Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson. Local Rademacher complexities. Annals of Statistics, 33(4):1497-1537, 2005. [ bib | .ps | .pdf | Abstract ]
[109] Rafael Jiménez-Rodriguez, Nicholas Sitar, and Peter L. Bartlett. Maximum likelihood estimation of trace length distribution parameters using the EM algorithm. In G. Barla and M. Barla, editors, Prediction, Analysis and Design in Geomechanical Applications: Proceedings of the Eleventh International Conference on Computer Methods and Advances in Geomechanics (IACMAG-2005), volume 1, pages 619-626, Bologna, 2005. Pàtron Editore. [ bib ]
[110] Peter L. Bartlett, Michael Collins, Ben Taskar, and David McAllester. Exponentiated gradient algorithms for large-margin structured classification. In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Advances in Neural Information Processing Systems 17, pages 113-120, Cambridge, MA, 2005. MIT Press. [ bib | .ps.gz | .pdf | Abstract ]
[111] Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. In Proceedings of the 18th Annual Conference on Learning Theory, volume 3559, pages 143-157. Springer, 2005. [ bib | .pdf ]
[112] 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 ]
[113] 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 ]
[114] Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Comment. Statistical Science, 21(3):341-346, 2006. [ bib ]
[115] Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Technical report, U. C. Berkeley, 2006. [ bib ]
[116] 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 ]
[117] Peter L. Bartlett and Shahar Mendelson. Empirical minimization. Probability Theory and Related Fields, 135(3):311-334, 2006. [ bib | .ps.gz | .pdf | Abstract ]
[118] 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 ]
[119] 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 ]
[120] 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 ]
[121] 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 ]
[122] 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 ]
[123] Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Journal of Machine Learning Research, 8:2347-2368, 2007. [ bib | .pdf | Abstract ]
[124] 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 ]
[125] 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 ]
[126] 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 ]
[127] 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 ]
[128] 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 ]
[129] 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 ]
[130] 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 ]
[131] 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 ]
[132] 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 ]
[133] 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 ]
[134] 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 ]
[135] 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 ]
[136] 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 ]
[137] 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 ]
[138] 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 ]
[139] 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 ]
[140] 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 ]
[141] 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 ]
[142] 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 ]
[143] 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 ]
[144] 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 ]
[145] 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 ]
[146] 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 ]
[147] 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 ]
[148] 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 ]
[149] 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 ]
[150] 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 ]
[151] 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 ]
[152] 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 ]
[153] 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 ]
[154] 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 ]
[155] 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 ]
[156] 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 ]
[157] 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 ]
[158] 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 ]
[159] 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 ]
[160] Peter L. Bartlett. Learning to act in uncertain environments. Communications of the ACM, 53(5):98, May 2010. [ bib | DOI ]
[161] 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 ]
[162] 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 ]
[163] 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 ]
[164] 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 ]
[165] 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 ]
[166] Sylvain Arlot and Peter L. Bartlett. Margin-adaptive model selection in statistical learning. Bernoulli, 17(2):687-713, May 2011. [ bib | .pdf ]
[167] 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 ]
[168] 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 ]
[169] 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 ]
[170] 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 ]
[171] 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 ]
[172] 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 ]
[173] 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 ]
[174] 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 ]
[175] 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 ]
[176] 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 ]
[177] 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 ]
[178] 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 ]
[179] 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 ]
[180] Ambuj Tewari and Peter L. Bartlett. Learning theory. In E-Reference - Signal Processing. Elsevier, 2013. Chapter 27. To appear. [ bib | Abstract ]

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