11. COLT 1998: Madison, Wisconsin, USA

Session 1

• Nicolò Cesa-Bianchi, Gábor Lugosi:
  On Sequential Prediction of Individual Sequences Relative to a Set of Experts. 1-11
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• V. G. Vovk, Chris Watkins:
  Universal Portfolio Selection. 12-23
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• Mark Herbster, Manfred K. Warmuth:
  Tracking the Best Regressor. 24-31
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• Kenji Yamanishi:
  Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. 32-43
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Session 2

• John Case, Sanjay Jain, Matthias Ott, Arun Sharma, Frank Stephan:
  Robust Learning Aided by Context. 44-55
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• Jochen Nessel:
  Birds Can Fly. 56-63
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• Andrew R. Mitchell:
  Learnability of a Subclass of Extended Pattern Languages. 64-71
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• Léa Meyer:
  Aspects of Complexity of Conservative Probabilistic Learning. 72-78
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Invited Talk I

• Meir Feder:
  Learning to Communicate via Unknown Channel (Abstract). 79
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Session 3

• Robert E. Schapire, Yoram Singer:
  Improved Boosting Algorithms using Confidence-Rated Predictions. 80-91
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• Avrim Blum, Tom M. Mitchell:
  Combining Labeled and Unlabeled Sata with Co-Training. 92-100
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Invited Talk II

• Stuart J. Russell:
  Learning Agents for Uncertain Environments (Extended Abstract). 101-103
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Session 4

• Claudio Gentile, David P. Helmbold:
  Improved Lower Bounds for Learning from Noisy Examples: An Information-Theoretic Approach. 104-115
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• Philip M. Long:
  The complexity of learning according to two models of a drifting environment. 116-125
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• Philip M. Long:
  On the Sample Complexity of Learning Functions with Bounded Variation. 126-133
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• David Gamarnik:
  Efficient Learning of Monotone Concepts via Quadratic Optimization. 134-143
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Session 5

• Andreas Birkendorf, Norbert Klasner, Christian Kuhlmann, Hans-Ulrich Simon:
  Structural Results about Exact Learning with Unspecified Attribute Values. 144-153
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• Roni Khardon:
  Learning First Order Universal Horn Expressions. 154-165
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• Irene Tsapara, György Turán:
  Learning Atomic Formulas with Prescribed Properties. 166-174
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• Thomas R. Amoth, Paul Cull, Prasad Tadepalli:
  Exact Learning of Tree Patterns from Queries and Counterexamples. 175-186
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Session 6

• Sanjay Jain, Carl H. Smith, Rolf Wiehagen:
  On the Power of Learning Robustly. 187-197
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• Rüdiger Reischuk, Thomas Zeugmann:
  Learning One-Variable Pattern Languages in Linear Average Time. 198-208
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Session 7

• Yoav Freund, Robert E. Schapire:
  Large Margin Classification Using the Perceptron Algorithm. 209-217
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• Martin Anthony, Sean B. Holden:
  Cross-Validation for Binary Classification by Real-Valued Functions: Theoretical Analysis. 218-229
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• David A. McAllester:
  Some PAC-Bayesian Theorems. 230-234
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• Matthias Miltrup, Georg Schnitger:
  Neural Networks and Efficient Associative Memory. 235-246
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Session 8

• Yoav Freund:
  Self Bounding Learning Algorithms. 247-258
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• Christopher D. Rosin:
  Sample Complexity of Model-Based Search. 259-267
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• Michael J. Kearns, Dana Ron:
  Testing Problems with Sub-Learning Sample Complexity. 268-279
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• Baruch Awerbuch, Stephen G. Kobourov:
  Polylogarithmic-Overhead Piecemeal Graph Exploration. 280-286
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Session 9

• Leslie G. Valiant:
  Projection Learning. 287-293
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• Amos Beimel, Felix Geller, Eyal Kushilevitz:
  The Query Complexity of Finding Local Minima in the Lattice. 294-302
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