1. COLT 1988: MIT, MA, USA

• J. Stephen Judd:
  Learning in Neural Networks. 2-8
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• Avrim Blum, Ronald L. Rivest:
  Training a 3-Node Neural Network is NP-Complete. 9-18
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• P. Raghavan:
  Learning in Threshold Networks. 19-27
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• Leslie G. Valiant:
  Functionality in Neural Nets. 28-39
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• David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth:
  Equivalence of Models for Polynomial Learnability. 42-55
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• Nathan Linial, Yishay Mansour, Ronald L. Rivest:
  Results on Learnability and the Vapnick-Chervonenkis Dimension. 56-68
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• Ronald L. Rivest, Robert H. Sloan:
  Learning Complicated Concepts Reliably and Usefully. 69-79
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• Gyora M. Benedek, Alon Itai:
  Learnability by Fixed Distributions. 80-90
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• Robert H. Sloan:
  Types of Noise in Data for Concept Learning. 91-96
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• George Shackelford, Dennis Volper:
  Learning k-DNF with Noise in the Attributes. 97-103
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• Jeffrey Scott Vitter, Jyh-Han Lin:
  Learning in Parallel. 106-124
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• Stéphane Boucheron, Jean Sallantin:
  Some Remarks About Space-Complexity of Learning, and Circuit Complexity of Recognizing. 125-138
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• Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant:
  A General Lower Bound on the Number of Examples Needed for Learning. 139-154
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• Haim Schweitzer:
  Non-Learnable Classes of Boolean Formulae That Are Closer Under Variable Permutation. 155-166
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• Dana Angluin:
  Learning With Hints. 167-181
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• Andrzej Ehrenfeucht, David Haussler:
  Learning Decision Trees from Random Examples. 182-194
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• John Case:
  The Power of Vacillation. 196-205
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• Stuart A. Kurtz, James S. Royer:
  Prudence in Language Learning. 206-219
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• Robert P. Daley:
  Transformation of Probabilistic Learning Strategies into Deterministic Learning Strategies. 220-226
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• William I. Gasarch, Carl H. Smith:
  Learning via Queries. 227-241
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• William I. Gasarch, Ramesh K. Sitaraman, Carl H. Smith, Mahendran Velauthapillai:
  Learning Programs with an Easy to Calculate Set of Errors. 242-250
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• John C. Cherniavsky, Mahendran Velauthapillai, Richard Statman:
  Inductive Inference: An Abstract Approach. 251-266
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• Ranan B. Banerji:
  Learning Theories in a Subset of a Polyadic Logic. 267-278
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• David Haussler, Nick Littlestone, Manfred K. Warmuth:
  Predicting {0, 1}-Functions on Randomly Drawn Points. 280-296
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• Philip D. Laird:
  Efficient Unsupervised Learning. 297-311
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• Alfredo De Santis, George Markowsky, Mark N. Wegman:
  Learning Probabilistic Prediction Functions. 312-328
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• Yasubumi Sakakibara:
  Learning Context-Free Grammars from Structural Data in Polynomial Time. 330-344
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• Assaf Marron:
  Learning Pattern Languages from a Single Initial Example and from Queries. 345-358
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• Ming Li, Umesh V. Vazirani:
  On the Learnability of Finite Automata. 359-370
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• Oscar H. Ibarra, Tao Jiang:
  Learning Regular Languages From Counterexamples. 371-385
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• Sara Porat, Jerome A. Feldman:
  Learning Automata from Ordered Examples. 386-396
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