| [1] | P. L. Bartlett. Vapnik-Chervonenkis dimension bounds for two- and three-layer networks. Neural Computation, 5(3):371--373, 1993. [ bib ] |
| [2] | 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 ] |
| [3] | 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 ] |
| [4] | 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 ] |
| [5] | 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 ] |
| [6] | 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 ] |
| [7] | 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 ] |
| [8] | 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 ] |
| [9] | 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 ] |
| [10] | 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 ] |
| [11] | 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 ] |
| [12] | 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 ] |
| [13] | 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 ] |
| [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] | 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 ] |
| [16] | 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 ] |
| [17] | 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 ] |
| [18] | Peter L. Bartlett, Anthony Burkitt, and Robert C. Williamson, editors. Proceedings of the Seventh Australian Conference on Neural Networks. Australian National University, 1996. [ bib ] |
| [19] | 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 ] |
| [20] | 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 ] |
| [21] | 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 ] |
| [22] | 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 ] |
| [23] | 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, pages 344--350, 1996. [ bib ] |
| [24] | 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 ] |
| [25] | 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 ] |
| [26] | 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 ] |
| [27] | 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 ] |
| [28] | 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 ] |
| [29] | 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 ] |
| [30] | 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 ] |
| [31] | P. L. Bartlett. Book review: `Neural networks for pattern recognition,' Christopher M. Bishop. Statistics in Medicine, 16(20):2385--2386, 1997. [ bib ] |
| [32] | 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 ] |
| [33] | 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 ] |
| [34] | 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 ] |
| [35] | 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 ] |
| [36] | 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 ] |
| [37] | 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 ] |
| [38] | P. L. Bartlett. Neural network learning. (abstract of invited talk.). In CONTROL 97 Conference Proceedings, Institution of Engineers Australia, page 543, 1997. [ bib ] |
| [39] | 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 ] |
| [40] | Peter L. Bartlett and Yishay Mansour, editors. Proceedings of the Eleventh Annual Conference on Computational Learning Theory. ACM Press, 1998. [ bib ] |
| [41] | 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 ] |
| [42] | 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 ] |
| [43] | 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 ] |
| [44] | 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 ] |
| [45] | 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 ] |
| [46] | 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 ] |
| [47] | P. L. Bartlett and M. Vidyasagar. Introduction to the special issue on learning theory. Systems and Control Letters, 34:113--114, 1998. [ bib ] |
| [48] | L. C. Kammer, R. R. Bitmead, and P. L. Bartlett. Optimal controller properties from closed-loop experiments. Automatica, 34(1):83--91, 1998. [ bib ] |
| [49] | 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 ] |
| [50] | 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 ] |
| [51] | 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 ] |
| [52] | 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 ] |
| [53] | 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 ] |
| [54] | 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 ] |
| [55] | 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 ] |
| [56] | Martin Anthony and Peter L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, 1999. 404pp. ISBN 978-0-521-57353-X. Reprinted 2001, 2002. Paperback edition 2009; ISBN 978-0-521-11862-0. [ bib | .html ] |
| [57] | 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 ] |
| [58] | 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 ] |
| [59] | 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 ] |
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| [61] | 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 ] |
| [62] | 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 ] |
| [63] | 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 ] |
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| [65] | 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 ] |
| [66] | 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 ] |
| [67] | 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 ] |
| [68] | Alexander J. Smola, Peter L. Bartlett, Bernard Schölkopf, and Dale Schuurmans, editors. Advances in Large Margin Classifiers. MIT Press, 2000. [ bib ] |
| [69] | M. Anthony and P. L. Bartlett. Function learning from interpolation. Combinatorics, Probability, and Computing, 9:213--225, 2000. [ bib ] |
| [70] | 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 ] |
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| [73] | 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 ] |
| [74] | B. Schölkopf, A. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12(5):1207--1245, 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] | L. Mason, P. L. Bartlett, and J. Baxter. Improved generalization through explicit optimization of margins. Machine Learning, 38(3):243--255, 2000. [ bib ] |
| [77] | 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 ] |
| [78] | 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 ] |
| [79] | 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 ] |
| [80] | 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 ] |
| [81] | 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 ] |
| [82] | 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 ] |
| [83] | J. Baxter and P. L. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Research, 15:319--350, 2001. [ bib | .html ] |
| [84] | 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 ] |
| [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] | 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 ] |
| [87] | 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 ] |
| [88] | 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 ] |
| [89] | 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 ] |
| [90] | P. L. Bartlett, S. Boucheron, and G. Lugosi. Model selection and error estimation. Machine Learning, 48:85--113, 2002. [ bib | .ps.gz ] |
| [91] | 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 ] |
| [92] | 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 ] |
| [93] | P. L. Bartlett, P. Fischer, and K.-U. Höffgen. Exploiting random walks for learning. Information and Computation, 176(2):121--135, 2002. [ bib | http ] |
| [94] | 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 ] |
| [95] | 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 ] |
| [96] | 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 ] |
| [97] | 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 ] |
| [98] | 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 ] |
| [99] | 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 ] |
| [100] | 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 ] |
| [101] | 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 ] |
| [102] | 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 ] |
| [103] | 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 ] |
| [104] | 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 ] |
| [105] | 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 ] |
| [106] | 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 ] |
| [107] | 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 ] |
| [108] | 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 ] |
| [109] | 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 ] |
| [110] | 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 ] |
| [111] | Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson. Local Rademacher complexities. Annals of Statistics, 33(4):1497--1537, 2005. [ bib | .ps | .pdf | Abstract ] |
| [112] | 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 ] |
| [113] | Peter L. Bartlett and Shahar Mendelson. Empirical minimization. Probability Theory and Related Fields, 135(3):311--334, 2006. [ bib | .ps.gz | .pdf | Abstract ] |
| [114] | 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 ] |
| [115] | Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Technical report, U. C. Berkeley, 2006. [ bib ] |
| [116] | Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe. Comment. Statistical Science, 21(3):341--346, 2006. [ bib ] |
| [117] | 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 ] |
| [118] | 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 ] |
| [119] | 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 ] |
| [120] | 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 ] |
| [121] | 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 ] |
| [122] | 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 ] |
| [123] | 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 ] |
| [124] | 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 ] |
| [125] | 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 ] |
| [126] | 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 ] |
| [127] | 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 ] |
| [128] | Peter L. Bartlett and Mikhail Traskin. Adaboost is consistent. Journal of Machine Learning Research, 8:2347--2368, 2007. [ bib | .pdf | .pdf | Abstract ] |
| [129] | 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 ] |
| [130] | David Rosenberg and Peter L. Bartlett. On bounds for Bayesian sequence prediction with non-Gaussian priors. Technical report, 2007. Technical Report. [ bib | Abstract ] |
| [131] | 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 ] |
| [132] | 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 ] |
| [133] | 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 ] |
| [134] | 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 ] |
| [135] | 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 ] |
| [136] | 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 ] |
| [137] | 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 ] |
| [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] | 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 ] |
| [140] | 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 ] |
| [141] | 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 ] |
| [142] | 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 ] |
| [143] | 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 ] |
| [144] | 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 ] |
| [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. [ bib | .pdf ] |
| [146] | 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 ] |
| [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] | 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 ] |
| [149] | 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 ] |
| [150] | 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 ] |
| [151] | 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 ] |
| [152] | Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft. Learning in a large function space: Privacy preserving mechanisms for SVM learning. Technical Report 0911.5708, arxiv.org, 2009. [ bib | http | 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] | 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 ] |
| [155] | 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 ] |
| [156] | 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 ] |
| [157] | 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 ] |
| [158] | 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 ] |
| [159] | 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 ] |
| [160] | 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 ] |
| [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] | Peter L. Bartlett. Learning to act in uncertain environments. Communications of the ACM, 53(5):98, May 2010. (Invited one-page comment). [ bib | DOI ] |
| [163] | 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 ] |
| [164] | Brian Kulis and Peter L. Bartlett. Implicit online learning. In Johannes Fürnkranz and Thorsten Joachims, editors, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 575--582, June 2010. [ bib | .pdf ] |
| [165] | 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 ] |
| [166] | 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 ] |
| [167] | 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. (Plenary talk abstract). [ bib | DOI ] |
| [168] | Sylvain Arlot and Peter L. Bartlett. Margin-adaptive model selection in statistical learning. Bernoulli, 17(2):687--713, May 2011. [ bib | .pdf | Abstract ] |
| [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 | Abstract ] |
| [170] | 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 | Abstract ] |
| [171] | 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 ] |
| [172] | 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 ] |
| [173] | 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 ] |
| [174] | John C. Duchi, Peter L. Bartlett, and Martin J. Wainwright. Randomized Smoothing for (Parallel) Stochastic Optimization. In 2012 IEEE 51st Annual Conference on Decision and Control (CDC), IEEE Conference on Decision and Control, pages 5442--5444, 345 E 47th St, New York, NY 10017 USA, 2012. IEEE. [ bib | Abstract ] |
| [175] | 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), volume 22, pages 504--510, April 2012. [ bib | .pdf | Abstract ] |
| [176] | 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 | .pdf | Abstract ] |
| [177] | 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 ] |
| [178] | 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 ] |
| [179] | 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 | .pdf | Abstract ] |
| [180] | 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 | Abstract ] |
| [181] | 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 ] |
| [182] | 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 ] |
| [183] | Peter L. Bartlett, Fernando Pereira, Chris J. C. Burges, Léon Bottou, and Kilian Q. Weinberger, editors. Advances in Neural Information Processing Systems 25. Proceedings of the 2012 Conference. NIPS Foundation, December 2012. [ bib | .html ] |
| [184] | Peter L. Bartlett, Peter Grunwald, Peter Harremoes, Fares Hedayati, and Wojciech Kotlowski. Horizon-independent optimal prediction with log-loss in exponential families. In Proceedings of the Conference on Learning Theory (COLT2013), volume 30, pages 639--661, 2013. [ bib | .pdf | Abstract ] |
| [185] | Yevgeny Seldin, Koby Crammer, and Peter L Bartlett. Open problem: Adversarial multiarmed bandits with limited advice. In Proceedings of the Conference on Learning Theory (COLT2013), volume 30, pages 1067--1072, 2013. [ bib | .pdf ] |
| [186] | Jacob Abernethy, Peter L. Bartlett, Rafael Frongillo, and Andre Wibisono. How to hedge an option against an adversary: Black-Scholes pricing is minimax optimal. In Advances in Neural Information Processing Systems 26, pages 2346--2354, 2013. [ bib | http | .pdf | Abstract ] |
| [187] | Yasin Abbasi-Yadkori, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, and Csaba Szepesvari. Online learning in Markov decision processes with adversarially chosen transition probability distributions. In Advances in Neural Information Processing Systems 26, pages 2508--2516, 2013. [ bib | http | .pdf | Abstract ] |
| [188] | Ambuj Tewari and Peter L. Bartlett. Learning theory. In Paulo S.R. Diniz, Johan A.K. Suykens, Rama Chellappa, and Sergios Theodoridis, editors, Signal Processing Theory and Machine Learning, volume 1 of Academic Press Library in Signal Processing, pages 775--816. Elsevier, 2014. [ bib ] |
| [189] | Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, and Yasin Abbasi-Yadkori. Prediction with limited advice and multiarmed bandits with paid observations. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 280--287, 2014. [ bib | .html | .pdf | Abstract ] |
| [190] | Yasin Abbasi-Yadkori, Peter L. Bartlett, and Varun Kanade. Tracking adversarial targets. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 369--377, 2014. [ bib | .html | .pdf | Abstract ] |
| [191] | J. Hyam Rubinstein, Benjamin Rubinstein, and Peter Bartlett. Bounding embeddings of VC classes into maximum classes. Technical Report 1401.7388, arXiv.org, 2014. [ bib | http | Abstract ] |
| [192] | J. Hyam Rubinstein, Benjamin Rubinstein, and Peter Bartlett. Bounding embeddings of VC classes into maximum classes. In A. Gammerman and V. Vovk, editors, Festschrift of Alexey Chervonenkis. Springer, 2014. [ bib | http | Abstract ] |
| [193] | Yasin Abbasi-Yadkori, Peter L. Bartlett, and Alan Malek. Linear programming for large-scale Markov decision problems. Technical Report 1402.6763, arXiv.org, 2014. [ bib | http | Abstract ] |
| [194] | Yasin Abbasi-Yadkori, Peter L. Bartlett, and Alan Malek. Linear programming for large-scale Markov decision problems. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 496--504, 2014. [ bib | .html | .pdf | Abstract ] |
| [195] | Wouter M Koolen, Alan Malek, and Peter L Bartlett. Efficient minimax strategies for square loss games. In Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 3230--3238. Curran Associates, Inc., 2014. [ bib | .pdf | Abstract ] |
| [196] | Alex Kantchelian, Michael C Tschantz, Ling Huang, Peter L Bartlett, Anthony D Joseph, and J. Doug Tygar. Large-margin convex polytope machine. In Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 3248--3256. Curran Associates, Inc., 2014. [ bib | .pdf | Abstract ] |
| [197] | Wouter Koolen, Alan Malek, Peter L. Bartlett, and Yasin Abbasi-Yadkori. Minimax time series prediction. In C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2548--2556. Curran Associates, Inc., 2015. [ bib | .pdf | Abstract ] |
| [198] | Yasin Abbasi-Yadkori, Wouter Koolen, Alan Malek, and Peter L. Bartlett. Minimax time series prediction. Technical report, EECS Department, University of California, Berkeley, 2015. [ bib ] |
| [199] | Walid Krichene, Alexandre Bayen, and Peter L. Bartlett. Accelerating mirror descent in continuous and discrete time. Technical report, EECS Department, University of California, Berkeley, 2015. [ bib ] |
| [200] | Walid Krichene, Alexandre Bayen, and Peter L. Bartlett. Accelerating mirror descent in continuous and discrete time. In C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, R. Garnett, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2827--2835. Curran Associates, Inc., 2015. [ bib | .pdf | Abstract ] |
| [201] | Yasin Abbasi-Yadkori, Peter L. Bartlett, and Stephen Wright. A Lagrangian relaxation approach to Markov decision problems. Technical report, UC Berkeley EECS, 2015. [ bib | Abstract ] |
| [202] | Peter L. Bartlett. Online prediction. Technical report, UC Berkeley EECS, 2015. [ bib | .pdf | Abstract ] |
| [203] | Yasin Abbasi-Yadkori, Peter L Bartlett, Xi Chen, and Alan Malek. Large-scale Markov decision problems with KL control cost. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), volume 37, pages 1053--1062, June 2015. [ bib | .html | .pdf | Abstract ] |
| [204] | Peter L. Bartlett, Wouter Koolen, Alan Malek, Eiji Takimoto, and Manfred Warmuth. Minimax fixed-design linear regression. In Proceedings of the Conference on Learning Theory (COLT2015), volume 40, pages 226--239, June 2015. [ bib | .pdf | .pdf | Abstract ] |
| [205] | Yasin Abbasi-Yadkori, Peter L. Bartlett, and Stephen Wright. A fast and reliable policy improvement algorithm. In Proceedings of AISTATS 2016, pages 1338--1346, 2016. [ bib | .html | .pdf | Abstract ] |
| [206] | Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, and Peter L. Bartlett. Improved learning complexity in combinatorial pure exploration bandits. In Proceedings of AISTATS 2016, pages 1004--1012, 2016. [ bib | .html | .pdf | Abstract ] |
| [207] | Walid Krichene, Alexandre Bayen, and Peter L. Bartlett. Adaptive averaging in accelerated descent dynamics. In Advances in Neural Information Processing Systems 29, pages 2991--2999, 2016. [ bib | http | .pdf | Abstract ] |
| [208] | Yasin Abbasi-Yadkori, Alan Malek, Peter L. Bartlett, and Victor Gabillon. Hit-and-run for sampling and planning in non-convex spaces. In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 888--895, Fort Lauderdale, FL, USA, 2017. [ bib | .pdf | Abstract ] |
| [209] | Kai Zhong, Zhao Song, Prateek Jain, Peter L. Bartlett, and Inderjit S. Dhillon. Recovery guarantees for one-hidden-layer neural networks. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning (ICML-17), volume 70 of Proceedings of Machine Learning Research, pages 4140--4149. PMLR, 2017. [ bib | .html | .pdf | Abstract ] |
| [210] | Martin Péron, Kai Helge Becker, Peter L. Bartlett, and Iadine Chadès. Fast-tracking stationary MOMDPs for adaptive management problems. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), pages 4531--4537, 2017. [ bib | http | http | Abstract ] |
| [211] | Yasin Abbasi-Yadkori, Peter L. Bartlett, and Victor Gabillon. Near minimax optimal players for the finite-time 3-expert prediction problem. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 3033--3042. Curran Associates, Inc., 2017. [ bib | .pdf | .pdf | Abstract ] |
| [212] | Peter Bartlett, Dylan Foster, and Matus Telgarsky. Spectrally-normalized margin bounds for neural networks. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 6240--6249. Curran Associates, Inc., 2017. [ bib | .pdf | .pdf | Abstract ] |
| [213] | Peter L. Bartlett, Nick Harvey, Chris Liaw, and Abbas Mehrabian. Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks. Technical Report 1703.02930, arXiv.org, 2017. [ bib | http | .pdf | Abstract ] |
| [214] | Walid Krichene and Peter Bartlett. Acceleration and averaging in stochastic descent dynamics. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 6796--6806. Curran Associates, Inc., 2017. [ bib | .pdf | .pdf | Abstract ] |
| [215] | Niladri Chatterji and Peter Bartlett. Alternating minimization for dictionary learning with random initialization. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 1997--2006. Curran Associates, Inc., 2017. [ bib | .pdf | .pdf ] |
| [216] | Fares Hedayati and Peter L. Bartlett. Exchangeability characterizes optimality of sequential normalized maximum likelihood and Bayesian prediction. IEEE Transactions on Information Theory, 63(10):6767--6773, October 2017. [ bib | DOI | .pdf | .pdf | Abstract ] |
| [217] | Peter L. Bartlett, Steven Evans, and Philip M. Long. Representing smooth functions as compositions of near-identity functions with implications for deep network optimization. Technical Report 1804.05012, arXiv.org, 2018. [ bib | http | .pdf | Abstract ] |
| [218] | Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, and Peter Bartlett. Gradient diversity: a key ingredient for scalable distributed learning. In Amos Storkey and Fernando Perez-Cruz, editors, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, volume 84 of Proceedings of Machine Learning Research, pages 1998--2007. PMLR, 2018. [ bib | .html | .pdf | Abstract ] |
| [219] | Xiang Cheng, Fred Roosta, Stefan Palombo, Peter Bartlett, and Michael Mahoney. Flag n’ flare: Fast linearly-coupled adaptive gradient methods. In Amos Storkey and Fernando Perez-Cruz, editors, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, volume 84 of Proceedings of Machine Learning Research, pages 404--414. PMLR, 2018. [ bib | .html | .pdf | Abstract ] |
| [220] | Xiang Cheng and Peter Bartlett. Convergence of Langevin MCMC in KL-divergence. In Firdaus Janoos, Mehryar Mohri, and Karthik Sridharan, editors, Proceedings of ALT2018, volume 83 of Proceedings of Machine Learning Research, pages 186--211. PMLR, 2018. [ bib | .html | .pdf | Abstract ] |
| [221] | Martin Péron, Peter Bartlett, Kai Helge Becker, Kate Helmstedt, and Iadine Chadès. Two approximate dynamic programming algorithms for managing complete SIS networks. In ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS 2018), 2018. [ bib | http | .pdf ] |
| [222] | Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett. Byzantine-robust distributed learning: Towards optimal statistical rates. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning (ICML-18), volume 80 of Proceedings of Machine Learning Research, pages 5650--5659. PMLR, 2018. [ bib | .html | .pdf | Abstract ] |
| [223] | Niladri Chatterji, Nicolas Flammarion, Yian Ma, Peter Bartlett, and Michael Jordan. On the theory of variance reduction for stochastic gradient Monte Carlo. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning (ICML-18), volume 80 of Proceedings of Machine Learning Research, pages 764--773. PMLR, 2018. [ bib | .html | .pdf | Abstract ] |
| [224] | Peter L. Bartlett, David P. Helmbold, and Philip M. Long. Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning (ICML-18), volume 80 of Proceedings of Machine Learning Research, pages 521--530. PMLR, 2018. [ bib | http | .pdf | Abstract ] |
| [225] | Alan Malek and Peter L. Bartlett. Horizon-independent minimax linear regression. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31, pages 5264--5273. Curran Associates, Inc., 2018. [ bib | .pdf | Abstract ] |
| [226] | Xiang Cheng, Niladri S. Chatterji, Peter L. Bartlett, and Michael I. Jordan. Underdamped Langevin MCMC: A non-asymptotic analysis. In Sébastien Bubeck, Vianney Perchet, and Philippe Rigollet, editors, Proceedings of the 31st Conference on Learning Theory (COLT2018), volume 75 of Proceedings of Machine Learning Research, pages 300--323. PMLR, 2018. [ bib | .html | .pdf | Abstract ] |
| [227] | Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon, Alan Malek, and Michal Valko. Best of both worlds: Stochastic and adversarial best-arm identification. In Sébastien Bubeck, Vianney Perchet, and Philippe Rigollet, editors, Proceedings of the 31st Conference on Learning Theory (COLT2018), volume 75 of Proceedings of Machine Learning Research, pages 918--949. PMLR, 2018. [ bib | http | .pdf | Abstract ] |
| [228] | Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, and Michael I. Jordan. Gen-Oja: Simple and efficient algorithm for streaming generalized eigenvector computation. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31, pages 7016--7025. Curran Associates, Inc., 2018. [ bib | .pdf | Abstract ] |
| [229] | Xiang Cheng, Niladri S. Chatterji, Yasin Abbasi-Yadkori, Peter L. Bartlett, and Michael I. Jordan. Sharp convergence rates for Langevin dynamics in the nonconvex setting. Technical Report arXiv:1805.01648 [stat.ML], arxiv.org, 2018. [ bib | http ] |
| [230] | Peter L. Bartlett, Nick Harvey, Christopher Liaw, and Abbas Mehrabian. Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks. Journal of Machine Learning Research, 20(63):1--17, 2019. [ bib | .html ] |
| [231] | Peter L. Bartlett, Victor Gabillon, and Michal Valko. A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. In Aurélien Garivier and Satyen Kale, editors, Proceedings of the 30th International Conference on Algorithmic Learning Theory, volume 98 of Proceedings of Machine Learning Research, pages 184--206, Chicago, Illinois, 2019. PMLR. [ bib | .html | .pdf | Abstract ] |
| [232] | Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, and Martin J. Wainwright. Derivative-free methods for policy optimization: Guarantees for linear quadratic systems. In Kamalika Chaudhuri and Masashi Sugiyama, editors, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), volume 89 of Proceedings of Machine Learning Research, pages 2916--2925. PMLR, 2019. [ bib | .html | .pdf | Abstract ] |
| [233] | Vidya Muthukumar, Mitas Ray, Anant Sahai, and Peter L. Bartlett. Best of many worlds: Robust model selection for online supervised learning. In Kamalika Chaudhuri and Masashi Sugiyama, editors, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), volume 89 of Proceedings of Machine Learning Research, pages 3177--3186. PMLR, 2019. [ bib | .html | .pdf | Abstract ] |
| [234] | Niladri Chatterji, Aldo Pacchiano, and Peter Bartlett. Online learning with kernel losses. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 971--980, Long Beach, California, USA, 2019. PMLR. [ bib | .html | .pdf | Abstract ] |
| [235] | Peter L. Bartlett, Victor Gabillon, Jennifer Healey, and Michal Valko. Scale-free adaptive planning for deterministic dynamics and discounted rewards. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 495--504, Long Beach, California, USA, 2019. PMLR. [ bib | .html | .pdf | Abstract ] |
| [236] | Yasin Abbasi-Yadkori, Peter L. Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvari, and Gellert Weisz. POLITEX: Regret bounds for policy iteration using expert prediction. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 3692--3702, Long Beach, California, USA, 2019. PMLR. [ bib | .html | .pdf | Abstract ] |
| [237] | Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter L. Bartlett. Defending against saddle point attack in Byzantine-robust distributed learning. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 7074--7084, Long Beach, California, USA, 2019. PMLR. [ bib | .html | .pdf | Abstract ] |
| [238] | Dong Yin, Ramchandran Kannan, and Peter L. Bartlett. Rademacher complexity for adversarially robust generalization. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 7085--7094, Long Beach, California, USA, 2019. PMLR. [ bib | .html | .pdf | Abstract ] |
| [239] | Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, and Michael I. Jordan. Bayesian robustness: A nonasymptotic viewpoint. Technical Report 1907.11826, arXiv, 2019. [ bib ] |
| [240] | Wenlong Mou, Nhat Ho, Martin J. Wainwright, Peter L. Bartlett, and Michael I. Jordan. Sampling for Bayesian mixture models: MCMC with polynomial-time mixing. Technical Report 1912.05153, arXiv, 2019. [ bib | Abstract ] |
| [241] | Yeshwanth Cherapanamjeri, Nicolas Flammarion, and Peter L. Bartlett. Fast mean estimation with sub-gaussian rates. In Alina Beygelzimer and Daniel Hsu, editors, Proceedings of the Thirty-Second Conference on Learning Theory, volume 99 of Proceedings of Machine Learning Research, pages 786--806. PMLR, 2019. [ bib | .html | .pdf | Abstract ] |
| [242] | Yeshwanth Cherapanamjeri and Peter L. Bartlett. Testing Markov chains without hitting. In Alina Beygelzimer and Daniel Hsu, editors, Proceedings of the 32nd Conference on Learning Theory (COLT2019), volume 99 of Proceedings of Machine Learning Research, pages 758--785. PMLR, 2019. [ bib | .html | .pdf | Abstract ] |
| [243] | Peter L. Bartlett, David P. Helmbold, and Philip M. Long. Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks. Neural Computation, 31:477--502, 2019. [ bib ] |
| [244] | Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, and Peter L. Bartlett. An efficient sampling algorithm for non-smooth composite potentials. Technical Report 1910.00551, arXiv, 2019. [ bib ] |
| [245] | Alexander Tsigler and Peter L. Bartlett. Benign overfitting in ridge regression. Technical Report arXiv:2009.14286, arxiv.org, 2020. [ bib | http | Abstract ] |
| [246] | Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett, Anca Dragan, and Martin Wainwright. Preference learning along multiple criteria: A game-theoretic perspective. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 7413--7424. Curran Associates, Inc., 2020. [ bib | .pdf | Abstract ] |
| [247] | Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, and Martin J. Wainwright. Derivative-free methods for policy optimization: Guarantees for linear quadratic systems. Journal of Machine Learning Research, 21(21):1--51, 2020. [ bib | .html ] |
| [248] | Niladri Chatterji, Vidya Muthukumar, and Peter L. Bartlett. OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 1844--1854. PMLR, 26--28 Aug 2020. [ bib | .html | .pdf | Abstract ] |
| [249] | Niladri Chatterji, Jelena Diakonikolas, Michael I. Jordan, and Peter L. Bartlett. Langevin Monte Carlo without smoothness. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 1716--1726. PMLR, 26--28 Aug 2020. [ bib | .html | .pdf | Abstract ] |
| [250] | Peter L. Bartlett, Philip M. Long, Gábor Lugosi, and Alexander Tsigler. Benign overfitting in linear regression. Proceedings of the National Academy of Sciences, 117(48):30063--30070, 2020. (arXiv:1906.11300). [ bib | DOI | arXiv | http | Abstract ] |
| [251] | Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, and Peter Bartlett. On approximate Thompson sampling with Langevin algorithms. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 6797--6807. PMLR, 13--18 Jul 2020. [ bib | .html | .pdf | Abstract ] |
| [252] | Jonathan Lee, Aldo Pacchiano, Peter L. Bartlett, and Michael I. Jordan. Accelerated message passing for entropy-regularized MAP inference. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 5736--5746. PMLR, 13--18 Jul 2020. [ bib | .html | .pdf | Abstract ] |
| [253] | Xiang Cheng, Dong Yin, Peter Bartlett, and Michael Jordan. Stochastic gradient and Langevin processes. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 1810--1819. PMLR, 13--18 Jul 2020. [ bib | .html | .pdf | Abstract ] |
| [254] | Hossein Mobahi, Mehrdad Farajtabar, and Peter L. Bartlett. Self-distillation amplifies regularization in Hilbert space. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33, volume 33, pages 3351--3361. Curran Associates, Inc., 2020. [ bib | .pdf | Abstract ] |
| [255] | Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, and Peter L. Bartlett. Optimal robust linear regression in nearly linear time. Technical Report 2007.08137, arXiv, 2020. [ bib ] |
| [256] | Wenlong Mou, Chris Junchi Li, Martin J Wainwright, Peter L Bartlett, and Michael I Jordan. On linear stochastic approximation: Fine-grained Polyak-Ruppert and non-asymptotic concentration. In Jacob Abernethy and Shivani Agarwal, editors, Proceedings of Thirty Third Conference on Learning Theory, volume 125 of Proceedings of Machine Learning Research, pages 2947--2997. PMLR, 2020. [ bib | .html | .pdf | Abstract ] |
| [257] | Peter L. Bartlett and Philip M. Long. Failures of model-dependent generalization bounds for least-norm interpolation. Technical Report arXiv:2010.08479, arxiv.org, 2020. [ bib | http | Abstract ] |
| [258] | Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, and Michael I. Jordan. Optimal mean estimation without a variance. Technical Report 2011.12433, arXiv, 2020. [ bib ] |
| [259] | Peter L. Bartlett, Andrea Montanari, and Alexander Rakhlin. Deep learning: a statistical viewpoint. Acta Numerica, 30:87–201, 2021. [ bib | DOI | http | Abstract ] |
| [260] | Peter L. Bartlett and Philip M. Long. Failures of model-dependent generalization bounds for least-norm interpolation. Journal of Machine Learning Research, 22(204):1--15, 2021. arXiv:2010.08479. [ bib | .html ] |
| [261] | Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, and Michael I. Jordan. High-order Langevin diffusion yields an accelerated MCMC algorithm. Journal of Machine Learning Research, 22(42):1--41, 2021. [ bib | .html ] |
| [262] | Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, and Michael I. Jordan. Is there an analog of Nesterov acceleration for gradient-based MCMC? Bernoulli, 27(3):1942--1992, 2021. [ bib | DOI | Abstract ] |
| [263] | Kush Bhatia, Peter L. Bartlett, Anca D. Dragan, and Jacob Steinhardt. Agnostic Learning with Unknown Utilities. In James R. Lee, editor, 12th Innovations in Theoretical Computer Science Conference (ITCS 2021), volume 185 of Leibniz International Proceedings in Informatics (LIPIcs), pages 55:1--55:20, Dagstuhl, Germany, 2021. Schloss Dagstuhl--Leibniz-Zentrum für Informatik. [ bib | DOI | http ] |
| [264] | Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, and Heinrich Jiang. Stochastic bandits with linear constraints. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 2827--2835. PMLR, 13--15 Apr 2021. [ bib | .html | .pdf | Abstract ] |
| [265] | Raman Arora, Peter L. Bartlett, Poorya Mianjy, and Nathan Srebro. Dropout: Explicit forms and capacity control. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 351--361. PMLR, 18--24 Jul 2021. [ bib | .html | .pdf | Abstract ] |
| [266] | Niladri S. Chatterji, Philip M. Long, and Peter L. Bartlett. When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations? In Mikhail Belkin and Samory Kpotufe, editors, Proceedings of the 34th Conference on Learning Theory (COLT2021), volume 134 of Proceedings of Machine Learning Research, pages 927--1027, 2021. [ bib | .html | Abstract ] |
| [267] | Juan Perdomo, Max Simchowitz, Alekh Agarwal, and Peter L. Bartlett. Towards a dimension-free understanding of adaptive linear control. In Proceedings of the 34th Conference on Learning Theory (COLT2021), 2021. [ bib | Abstract ] |
| [268] | Peter L. Bartlett, Sebastien Bubeck, and Yeshwanth Cherapanamjeri. Adversarial examples in multi-layer random ReLU networks. Technical Report 2106.12611, arXiv, 2021. [ bib | Abstract ] |
| [269] | Peter L. Bartlett, Sebastien Bubeck, and Yeshwanth Cherapanamjeri. Adversarial examples in multi-layer random ReLU networks. In Advances in Neural Information Processing Systems 34, 2021. [ bib | Abstract ] |
| [270] | Aldo Pacchiano, Jonathan Lee, Peter L. Bartlett, and Ofir Nachum. Near optimal policy optimization via reps. In Advances in Neural Information Processing Systems 34, 2021. [ bib | Abstract ] |
| [271] | Niladri Chatterji, Aldo Pacchiano, Peter L. Bartlett, and Michael I. Jordan. On the theory of reinforcement learning with once-per-episode feedback. In Advances in Neural Information Processing Systems 34, 2021. [ bib | Abstract ] |
| [272] | Niladri S. Chatterji, Philip M. Long, and Peter L. Bartlett. When does gradient descent with logistic loss find interpolating two-layer networks? Journal of Machine Learning Research, 22(159):1--48, 2021. [ bib | http | Abstract ] |
| [273] | Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, and Peter L. Bartlett. An efficient sampling algorithm for non-smooth composite potentials. Journal of Machine Learning Research, 23(233):1--50, 2022. [ bib | .html ] |
| [274] | Niladri S. Chatterji, Peter L. Bartlett, and Philip M. Long. Oracle lower bounds for stochastic gradient sampling algorithms. Bernoulli, 28(2):1074--1092, 2022. arXiv:2002.00291. [ bib | http | Abstract ] |
| [275] | Wenlong Mou, Nicolas Flammarion, Martin J. Wainwright, and Peter L. Bartlett. Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity. Bernoulli, 28(3):1577--1601, 2022. [ bib | DOI | http | Abstract ] |
| [276] | Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, and Peter L. Bartlett. Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. In Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022. [ bib | Abstract ] |
| [277] | Spencer Frei, Niladri Chatterji, and Peter L. Bartlett. Benign overfitting without linearity: Neural network classifiers trained by gradient descent for noisy linear data. In Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022. [ bib | Abstract ] |
| [278] | Peter L. Bartlett, Piotr Indyk, and Tal Wagner. Generalization bounds for data-driven numerical linear algebra. In Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022. [ bib | Abstract ] |
| [279] | Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, and Michael I. Jordan. Optimal mean estimation without a variance. In Proceedings of the 35th Conference on Learning Theory (COLT2022), 2022. [ bib | Abstract ] |
| [280] | Niladri S. Chatterji, Philip M. Long, and Peter L. Bartlett. The interplay between implicit bias and benign overfitting in two-layer linear networks. Journal of Machine Learning Research, 23(263):1--48, 2022. [ bib | .html ] |
| [281] | Wenlong Mou, Martin J. Wainwright, and Peter L. Bartlett. Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency. Technical Report 2209.13075, arXiv, 2022. [ bib | Abstract ] |
| [282] | Peter L. Bartlett, Philip M. Long, and Olivier Bousquet. The dynamics of sharpness-aware minimization: Bouncing across ravines and drifting towards wide minima. Technical Report 2210.01513, arXiv, 2022. [ bib | Abstract ] |
| [283] | Alexander Tsigler and Peter L. Bartlett. Benign overfitting in ridge regression. Journal of Machine Learning Research, 24(123):1--76, 2023. (See also arXiv:2009.14286). [ bib | .html ] |
| [284] | Spencer Frei, Niladri S. Chatterji, and Peter L. Bartlett. Random feature amplification: Feature learning and generalization in neural networks. Journal of Machine Learning Research, 24(303):1--49, 2023. [ bib | .html | Abstract ] |
| [285] | Wenlong Mou, Peng Ding, Martin J. Wainwright, and Peter L. Bartlett. Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency. Technical Report 2301.06240, arXiv, 2023. [ bib | Abstract ] |
| [286] | Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, and Wei Hu. Implicit bias in leaky ReLU networks trained on high-dimensional data. In Proceedings of ICLR 2023, 2023. To appear. [ bib | Abstract ] |
| [287] | Juan C. Perdomo, Akshay Krishnamurthy, Peter L. Bartlett, and Sham Kakade. A complete characterization of linear estimators for offline policy evaluation. Journal of Machine Learning Research, 24(284):1--50, 2023. [ bib | .html | Abstract ] |
| [288] | Aldo Pacchiano, Peter L. Bartlett, and Michael I. Jordan. An instance-dependent analysis for the cooperative multi-player multi-armed bandit. In Shipra Agrawal and Francesco Orabona, editors, Proceedings of the 34th International Conference on Algorithmic Learning Theory, volume 201 of Proceedings of Machine Learning Research, pages 1166--1215. PMLR, 2023. [ bib | .html | .pdf | Abstract ] |
| [289] | Spencer Frei, Gal Vardi, Peter L. Bartlett, and Nathan Srebro. Benign overfitting in linear classifiers and leaky ReLU networks from KKT conditions for margin maximization. In Proceedings of the 36th Conference on Learning Theory (COLT2023), 2023. To Appear. Also arXiv:2303.01462. [ bib | Abstract ] |
| [290] | Peter L. Bartlett, Philip M. Long, and Olivier Bousquet. The dynamics of sharpness-aware minimization: Bouncing across ravines and drifting towards wide minima. Journal of Machine Learning Research, 24(316):1--36, 2023. [ bib | .html | Abstract ] |
| [291] | Spencer Frei, Gal Vardi, Peter L. Bartlett, and Nathan Srebro. The double-edged sword of implicit bias: Generalization vs. robustness in ReLU networks. Technical Report 2303.01456, arXiv, 2023. [ bib | Abstract ] |
| [292] | Ruiqi Zhang, Spencer Frei, and Peter L. Bartlett. Trained transformers learn linear models in-context. Technical Report 2306.09927, arXiv, 2023. [ bib | Abstract ] |
| [293] | Philip M. Long and Peter L. Bartlett. Sharpness-aware minimization and the edge of stability. Technical Report 2309.12488, arXiv, 2023. [ bib ] |
| [294] | Spencer Frei, Gal Vardi, Peter L. Bartlett, and Nathan Srebro. The double-edged sword of implicit bias: Generalization vs. robustness in ReLU networks. In Advances in Neural Information Processing Systems 36, 2023. [ bib ] |
| [295] | Kush Bhatia, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, and Michael I. Jordan. Bayesian robustness: A nonasymptotic viewpoint. Journal of the American Statistical Association, 119(546):1112--1123, 2023. [ bib | DOI ] |
| [296] | Peter L. Bartlett and Philip M. Long. Corrigendum to 'prediction, learning, uniform convergence, and scale-sensitive dimensions'. Journal of Computer and System Sciences, 140:103465, 2024. [ bib ] |
| [297] | Wenlong Mou, Nhat Ho, Martin Wainwright, Peter L. Bartlett, and Michael Jordan. A diffusion process perspective on posterior contraction rates for parameters. SIAM Journal on Mathematics of Data Science, 2024. (To appear). [ bib ] |
| [298] | Ruiqi Zhang, Spencer Frei, and Peter L. Bartlett. Trained transformers learn linear models in-context. Journal of Machine Learning Research, 25(49):1--55, 2024. [ bib | .html | Abstract ] |
| [299] | Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, and Peter L. Bartlett. How many pretraining tasks are needed for in-context learning of linear regression? In The Twelfth International Conference on Learning Representations, 2024. [ bib | http ] |
| [300] | Saptarshi Chakraborty and Peter L. Bartlett. A statistical analysis of Wasserstein autoencoders for intrinsically low-dimensional data. In The Twelfth International Conference on Learning Representations, 2024. [ bib | http ] |
| [301] | Pierre Marion, Anna Korba, Peter L. Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, and Quentin Berthet. Implicit diffusion: Efficient optimization through stochastic sampling. Technical Report 2402.05468, arXiv, 2024. [ bib | Abstract ] |
| [302] | Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, and Bin Yu. Large stepsize gradient descent for logistic loss: Non-monotonicity of the loss improves optimization efficiency. In Proceedings of the 37th Conference on Learning Theory (COLT2024), 2024. (To appear). [ bib | Abstract ] |
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