12. COLT 1999: Santa Cruz, CA, USA

• Claudio Gentile, Nick Littlestone:
  The Robustness of the p-Norm Algorithms. 1-11
  Electronic Edition (ACM DL) BibTeX
• Nicolò Cesa-Bianchi, Gábor Lugosi:
  Minimax Regret Under log Loss for General Classes of Experts. 12-18
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• Tsachy Weissman, Neri Merhav:
  On Prediction of Individual Sequences Relative to a Set of Experts in the Presence of Noise. 19-28
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• Geoffrey J. Gordon:
  Regret Bounds for Prediction Problems. 29-40
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• Robert H. Sloan, György Turán:
  On Theory Revision with Queries. 41-52
  Electronic Edition (ACM DL) BibTeX
• Yoav Freund, Yishay Mansour:
  Estimating a Mixture of Two Product Distributions. 53-62
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• Stephen Kwek:
  An Apprentice Learning Model (extended abstract). 63-74
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• Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon:
  Uniform-Distribution Attribute Noise Learnability. 75-80
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• Nader H. Bshouty, David K. Wilson:
  On Learning in the Presence of Unspecified Attribute Values. 81-87
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• Paul W. Goldberg:
  Learning Fixed-Dimension Linear Thresholds from Fragmented Data. 88-99
  Electronic Edition (ACM DL) BibTeX
• David B. Shmoys:
  Approximation Algorithms for Clustering Problems. 100-102
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• Yoav Freund:
  An Adaptive Version of the Boost by Majority Algorithm. 102-113
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• Robert E. Schapire:
  Drifting Games. 114-124
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• John D. Lafferty:
  Additive Models, Boosting, and Inference for Generalized Divergences. 125-133
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• Jyrki Kivinen, Manfred K. Warmuth:
  Boosting as Entropy Projection. 134-144
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• Venkatesan Guruswami, Amit Sahai:
  Multiclass Learning, Boosting, and Error-Correcting Codes. 145-155
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• Tong Zhang:
  Theoretical Analysis of a Class of Randomized Regularization Methods. 156-163
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• David A. McAllester:
  PAC-Bayesian Model Averaging. 164-170
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• Peter Grünwald:
  Viewing all Models as ``Probabilistic''. 171-182
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• Yishay Mansour:
  Reinforcement Learning and Mistake Bounded Algorithms. 183-192
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• Vladislav Tadic:
  Convergence Analysis of Temporal-Difference Learning Algorithms with Linear Function Approximation. 193-202
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• Avrim Blum, Adam Kalai, John Langford:
  Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. 203-208
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• John Langford, Avrim Blum:
  Microchoice Bounds and Self Bounding Learning Algorithms. 209-214
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• Atsuyoshi Nakamura:
  Learning Specialist Decision Lists. 215-225
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• Yuri Kalnishkan:
  Linear Relations between Square-Loss and Kolmogorov Complexity. 226-232
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• Chamy Allenberg:
  Individual Sequence Prediction - Upper Bounds and Application for Complexity. 233-242
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• Sebastiaan Terwijn:
  Extensional Set Learning (extended abstract). 243-248
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• Sanjay Jain, Arun Sharma:
  On a Generalized Notion of Mistake Bounds. 249-256
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• Efim B. Kinber, Christophe Papazian, Carl H. Smith, Rolf Wiehagen:
  On the Intrinsic Complexity of Learning Recursive Functions. 257-266
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• Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson:
  Covering Numbers for Support Vector Machines. 267-277
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• John Shawe-Taylor, Nello Cristianini:
  Further Results on the Margin Distribution. 278-285
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• Nader H. Bshouty, Jeffrey C. Jackson, Christino Tamon:
  More Efficient PAC-Learning of DNF with Membership Queries Under the Uniform Distribution. 286-295
  Electronic Edition (ACM DL) BibTeX
• Rocco A. Servedio:
  On PAC Learning Using Winnow, Perceptron, and a Perceptron-like Algorithm. 296-307
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• David Gamarnik:
  Extension of the PAC Framework to Finite and Countable Markov Chains. 308-317
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• Elias Abboud, Nader Agha, Nader H. Bshouty, Nizar Radwan, Fathi Saleh:
  Learning Threshold Functions with Small Weights Using Membership Queries. 318-322
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• Thomas R. Amoth, Paul Cull, Prasad Tadepalli:
  Exact Learning of Unordered Tree Patterns from Queries. 323-332
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