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Title:
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Peter L. Bartlett: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Peter L. Bartlett, Shahar Mendelson
    Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. [Citation Graph (0, 0)][DBLP]
    COLT/EuroCOLT, 2001, pp:224-240 [Conf]
  2. Peter L. Bartlett, Shahar Mendelson, Petra Philips
    Local Complexities for Empirical Risk Minimization. [Citation Graph (0, 0)][DBLP]
    COLT, 2004, pp:270-284 [Conf]
  3. Peter L. Bartlett, Ambuj Tewari
    Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. [Citation Graph (0, 0)][DBLP]
    COLT, 2004, pp:564-578 [Conf]
  4. Peter L. Bartlett, Robert C. Williamson
    Investigating the Distribution Assumptions in the Pac Learning Model. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:24-32 [Conf]
  5. Peter L. Bartlett
    Learning With a Slowly Changing Distribution. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:243-252 [Conf]
  6. Peter L. Bartlett
    Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. [Citation Graph (0, 0)][DBLP]
    COLT, 1993, pp:144-150 [Conf]
  7. Peter L. Bartlett, Jonathan Baxter
    Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:133-141 [Conf]
  8. Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni
    Learning Changing Concepts by Exploiting the Structure of Change. [Citation Graph (0, 0)][DBLP]
    COLT, 1996, pp:131-139 [Conf]
  9. Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi
    Model Selection and Error Estimation. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:286-297 [Conf]
  10. Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson
    Localized Rademacher Complexities. [Citation Graph (0, 0)][DBLP]
    COLT, 2002, pp:44-58 [Conf]
  11. Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen
    Exploiting Random Walks for Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1994, pp:318-327 [Conf]
  12. Peter L. Bartlett, Philip M. Long
    More Theorems about Scale-sensitive Dimensions and Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1995, pp:392-401 [Conf]
  13. Peter L. Bartlett, Philip M. Long, Robert C. Williamson
    Fat-Shattering and the Learnability of Real-Valued Functions. [Citation Graph (0, 0)][DBLP]
    COLT, 1994, pp:299-310 [Conf]
  14. Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson
    Covering Numbers for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    COLT, 1999, pp:267-277 [Conf]
  15. Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
    Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. [Citation Graph (0, 0)][DBLP]
    COLT, 1994, pp:362-367 [Conf]
  16. Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
    On Efficient Agnostic Learning of Linear Combinations of Basis Functions. [Citation Graph (0, 0)][DBLP]
    COLT, 1995, pp:369-376 [Conf]
  17. Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
    The Importance of Convexity in Learning with Squared Loss. [Citation Graph (0, 0)][DBLP]
    COLT, 1996, pp:140-146 [Conf]
  18. John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony
    A Framework for Structural Risk Minimisation. [Citation Graph (0, 0)][DBLP]
    COLT, 1996, pp:68-76 [Conf]
  19. Ambuj Tewari, Peter L. Bartlett
    On the Consistency of Multiclass Classification Methods. [Citation Graph (0, 0)][DBLP]
    COLT, 2005, pp:143-157 [Conf]
  20. Martin Anthony, Peter L. Bartlett
    Function learning from interpolation. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1995, pp:211-221 [Conf]
  21. Peter L. Bartlett, Shai Ben-David
    Hardness Results for Neural Network Approximation Problems. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1999, pp:50-62 [Conf]
  22. Peter L. Bartlett, Tamás Linder, Gábor Lugosi
    A Minimax Lower Bound for Empirical Quantizer Design. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1997, pp:210-222 [Conf]
  23. Jonathan Baxter, Peter L. Bartlett
    A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1997, pp:251-259 [Conf]
  24. Jonathan Baxter, Peter L. Bartlett
    Reinforcement Learning in POMDP's via Direct Gradient Ascent. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:41-48 [Conf]
  25. Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
    Learning the Kernel Matrix with Semi-Definite Programming. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:323-330 [Conf]
  26. Peter L. Bartlett
    An Introduction to Reinforcement Learning Theory: Value Function Methods. [Citation Graph (0, 0)][DBLP]
    Machine Learning Summer School, 2002, pp:184-202 [Conf]
  27. Peter L. Bartlett
    For Valid Generalization the Size of the Weights is More Important than the Size of the Network. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:134-140 [Conf]
  28. Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester
    Exponentiated Gradient Algorithms for Large-margin Structured Classification. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  29. Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe
    Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  30. Peter L. Bartlett, Vitaly Maiorov, Ron Meir
    Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:190-196 [Conf]
  31. Jonathan Baxter, Peter L. Bartlett
    The Canonical Distortion Measure in Feature Space and 1-NN Classification. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  32. Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason
    Generalization in Decision Trees and DNF: Does Size Matter? [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  33. Evan Greensmith, Peter L. Bartlett, Jonathan Baxter
    Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1507-1514 [Conf]
  34. Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson
    Examples of learning curves from a modified VC-formalism. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:344-350 [Conf]
  35. Llew Mason, Peter L. Bartlett, Jonathan Baxter
    Direct Optimization of Margins Improves Generalization in Combined Classifiers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:288-294 [Conf]
  36. Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean
    Boosting Algorithms as Gradient Descent. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:512-518 [Conf]
  37. Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson
    Shrinking the Tube: A New Support Vector Regression Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:330-336 [Conf]
  38. Alex J. Smola, Peter L. Bartlett
    Sparse Greedy Gaussian Process Regression. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:619-625 [Conf]
  39. Robert C. Williamson, Peter L. Bartlett
    Splines, Rational Functions and Neural Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:1040-1047 [Conf]
  40. Martin Anthony, Peter L. Bartlett
    Function Learning From Interpolation. [Citation Graph (0, 0)][DBLP]
    Combinatorics, Probability & Computing, 2000, v:9, n:3, pp:- [Journal]
  41. Martin Anthony, Peter L. Bartlett, Yuval Ishai, John Shawe-Taylor
    Valid Generalisation from Approximate Interpolation. [Citation Graph (0, 0)][DBLP]
    Combinatorics, Probability & Computing, 1996, v:5, n:, pp:191-214 [Journal]
  42. Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen
    Exploiting Random Walks for Learning. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 2002, v:176, n:2, pp:121-135 [Journal]
  43. Jonathan Baxter, Peter L. Bartlett
    Infinite-Horizon Policy-Gradient Estimation. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2001, v:15, n:, pp:319-350 [Journal]
  44. Jonathan Baxter, Peter L. Bartlett, Lex Weaver
    Experiments with Infinite-Horizon, Policy-Gradient Estimation. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2001, v:15, n:, pp:351-381 [Journal]
  45. Peter L. Bartlett, Jonathan Baxter
    Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2002, v:64, n:1, pp:133-150 [Journal]
  46. Peter L. Bartlett, Philip M. Long
    Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1998, v:56, n:2, pp:174-190 [Journal]
  47. Peter L. Bartlett, Philip M. Long, Robert C. Williamson
    Fat-Shattering and the Learnability of Real-Valued Functions. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1996, v:52, n:3, pp:434-452 [Journal]
  48. Llew Mason, Peter L. Bartlett, Mostefa Golea
    Generalization Error of Combined Classifiers. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2002, v:65, n:2, pp:415-438 [Journal]
  49. Evan Greensmith, Peter L. Bartlett, Jonathan Baxter
    Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:1471-1530 [Journal]
  50. Peter L. Bartlett, Shahar Mendelson
    Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:463-482 [Journal]
  51. Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
    Learning the Kernel Matrix with Semidefinite Programming. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:27-72 [Journal]
  52. Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni
    Learning Changing Concepts by Exploiting the Structure of Change. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2000, v:41, n:2, pp:153-174 [Journal]
  53. Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi
    Model Selection and Error Estimation. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:48, n:1-3, pp:85-113 [Journal]
  54. Llew Mason, Peter L. Bartlett, Jonathan Baxter
    Improved Generalization Through Explicit Optimization of Margins. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2000, v:38, n:3, pp:243-255 [Journal]
  55. Peter L. Bartlett, Vitaly Maiorov, Ron Meir
    Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1998, v:10, n:8, pp:2159-2173 [Journal]
  56. Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
    Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1997, v:9, n:4, pp:765-769 [Journal]
  57. Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett
    New Support Vector Algorithms. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:5, pp:1207-1245 [Journal]
  58. Peter L. Bartlett, Shai Ben-David
    Hardness results for neural network approximation problems. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2002, v:284, n:1, pp:53-66 [Journal]
  59. Peter 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. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1998, v:44, n:2, pp:525-536 [Journal]
  60. Peter L. Bartlett, Sanjeev R. Kulkarni, S. E. Posner
    Covering numbers for real-valued function classes. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1997, v:43, n:5, pp:1721-1724 [Journal]
  61. Peter L. Bartlett, Tamás Linder, Gábor Lugosi
    The Minimax Distortion Redundancy in Empirical Quantizer Design. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1998, v:44, n:5, pp:1802-1813 [Journal]
  62. Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson
    Covering numbers for support vector machines. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2002, v:48, n:1, pp:239-250 [Journal]
  63. Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
    Efficient agnostic learning of neural networks with bounded fan-in. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1996, v:42, n:6, pp:2118-2132 [Journal]
  64. Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
    The Importance of Convexity in Learning with Squared Loss. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1998, v:44, n:5, pp:1974-1980 [Journal]
  65. John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony
    Structural Risk Minimization Over Data-Dependent Hierarchies. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 1998, v:44, n:5, pp:1926-1940 [Journal]
  66. Ambuj Tewari, Peter L. Bartlett
    Bounded Parameter Markov Decision Processes with Average Reward Criterion. [Citation Graph (0, 0)][DBLP]
    COLT, 2007, pp:263-277 [Conf]
  67. Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin
    Multitask Learning with Expert Advice. [Citation Graph (0, 0)][DBLP]
    COLT, 2007, pp:484-498 [Conf]
  68. Alexander Rakhlin, Jacob Abernethy, Peter L. Bartlett
    Online discovery of similarity mappings. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:767-774 [Conf]
  69. Peter L. Bartlett, Ambuj Tewari
    Sample Complexity of Policy Search with Known Dynamics. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:97-104 [Conf]
  70. Peter L. Bartlett, Mikhail Traskin
    AdaBoost is Consistent. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:105-112 [Conf]
  71. Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein
    Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1193-1200 [Conf]

  72. Optimal Online Prediction in Adversarial Environments. [Citation Graph (, )][DBLP]


  73. A Regularization Approach to Metrical Task Systems. [Citation Graph (, )][DBLP]


  74. Open problems in the security of learning. [Citation Graph (, )][DBLP]


  75. Optimal Stragies and Minimax Lower Bounds for Online Convex Games. [Citation Graph (, )][DBLP]


  76. High-Probability Regret Bounds for Bandit Online Linear Optimization. [Citation Graph (, )][DBLP]


  77. A Learning-Based Approach to Reactive Security. [Citation Graph (, )][DBLP]


  78. Implicit Online Learning. [Citation Graph (, )][DBLP]


  79. Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. [Citation Graph (, )][DBLP]


  80. Adaptive Online Gradient Descent. [Citation Graph (, )][DBLP]


  81. A Unifying View of Multiple Kernel Learning. [Citation Graph (, )][DBLP]


  82. Learning to act in uncertain environments: technical perspective. [Citation Graph (, )][DBLP]


  83. A Stochastic View of Optimal Regret through Minimax Duality [Citation Graph (, )][DBLP]


  84. Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning [Citation Graph (, )][DBLP]


  85. A Learning-Based Approach to Reactive Security [Citation Graph (, )][DBLP]


  86. A Unifying View of Multiple Kernel Learning [Citation Graph (, )][DBLP]


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