6. COLT 1993: Santa Cruz, CA, USA

• John J. Grefenstette:
  Genetic Algorithms and Machine Learning. 3-4
  
• Geoffrey E. Hinton, Drew van Camp:
  Keeping the Neural Networks Simple by Minimizing the Description Length of the Weights. 5-13
  
• Robert E. Schapire, Linda Sellie:
  Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. 17-26
  
• Vijay V. Raghavan, Dawn Wilkins:
  Learning µ-branching Programs with Queries. 27-36
  
• Ulf Berggren:
  Linear Time Deterministic Learning of k-Term DNF. 37-40
  
• Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock, Sleiman Matar:
  Asking Questions to Minimize Errors. 41-50
  
• Rodney G. Downey, Patricia A. Evans, Michael R. Fellows:
  Parameterized Learning Complexity. 51-57
  
• Sampath Kannan:
  On the Query Complexity of Learning. 58-66
  
• Sally A. Goldman, H. David Mathias:
  Teaching a Smart Learner. 67-76
  
• Klaus-Uwe Höffgen:
  Learning and Robust Learning of Product Distributions. 77-83
  
• Philip D. Laird, Ronald Saul, Peter Dunning:
  A Model of Sequence Extrapolation. 84-93
  
• Kenji Yamanishi:
  On Polynomial-Time Probably almost Discriminative Learnability. 94-100
  
• Michael J. Kearns, H. Sebastian Seung:
  Learning from a Population of Hypotheses. 101-110
  
• Sanjeev R. Kulkarni, Ofer Zeitouni:
  On Probably Correct Classification of Concepts. 111-116
  
• Martin Kummer, Frank Stephan:
  On the Structure of Degrees of Inferability. 117-126
  
• Steffen Lange, Thomas Zeugmann:
  Language Learning in Dependence on the Space of Hypotheses. 127-136
  
• Joe Kilian, Hava T. Siegelmann:
  On the Power of Sigmoid Neural Networks. 137-143
  
• Peter L. Bartlett:
  Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. 144-150
  
• Mostefa Golea, Mario Marchand:
  Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks. 151-157
  
• Martin Anthony, Sean B. Holden:
  On the Power of Polynomial Discriminators and Radial Basis Function Networks. 158-164
  
• Rusins Freivalds, Efim B. Kinber, Carl H. Smith:
  On the Impact of Forgetting on Learning Machines. 165-174
  
• Efim B. Kinber, Carl H. Smith, Mahendran Velauthapillai, Rolf Wiehagen:
  On Learning Multiple Concepts in Parallel. 175-181
  
• Robert P. Daley, Bala Kalyanasundaram:
  Capabilities of Probabilistic Learners with Bounded Mind Changes. 182-191
  
• Sanjay Jain, Arun Sharma:
  Probability is More Powerful Than Team for Language Identification from Positive Data. 192-198
  
• Robert P. Daley, Bala Kalyanasundaram, Mahendran Velauthapillai:
  Capabilities of fallible FINite Learning. 199-208
  
• Shai Ben-David, Michal Jacovi:
  On Learning in the Limit and Non-Uniform (epsilon, delta)-Learning. 209-217
  
• Dana Ron, Ronitt Rubinfeld:
  Learning Fallible Finite State Automata. 218-227
  
• Takashi Yokomori:
  Learning Two-Tape Automata from Queries and Counterexamples. 228-235
  
• Alvis Brazma:
  Efficient Identification of Regular Expressions from Representative Examples. 236-242
  
• Zhixiang Chen:
  Learning Unions of Two Rectangles in the Plane with Equivalence Queries. 243-252
  
• Peter Auer:
  On-Line Learning of Rectangles in Noisy Environments. 253-261
  
• Scott E. Decatur:
  Statistical Queries and Faulty PAC Oracles. 262-268
  
• William S. Evans, Sridhar Rajagopalan, Umesh V. Vazirani:
  Choosing a Reliable Hypothesis. 269-276
  
• Margrit Betke, Ronald L. Rivest, Mona Singh:
  Piecemeal Learning of an Unknown Environment. 277-286
  
• Shai Ben-David, Eli Dichterman:
  Learning with Restricted Focus of Attention. 287-296
  
• Tom Bylander:
  Polynomial Learnability of Linear Threshold Approximations. 297-302
  
• Christian Darken, Michael Donahue, Leonid Gurvits, Eduardo D. Sontag:
  Rate of Approximation Results Motivated by Robust Neural Network Learning. 303-309
  
• Shao C. Fang, Santosh S. Venkatesh:
  On the Average Tractability of Binary Integer Programming and the Curious Transition to Perfect Generalization in Learning Majority Functions. 310-316
  
• Eyal Kushilevitz, Dan Roth:
  On Learning Visual Concepts and DNF Formulae. 317-326
  
• Shai Ben-David, Michael Lindenbaum:
  Localization vs. Identification of Semi-Algebraic Sets. 327-336
  
• Avrim Blum, Prasad Chalasani, Jeffrey C. Jackson:
  On Learning Embedded Symmetric Concepts. 337-346
  
• Dan Boneh, Richard J. Lipton:
  Amplification of Weak Learning under the Uniform Distribution. 347-351
  
• Thomas R. Hancock:
  Learning kµ Decision Trees on the Uniform Distribution. 352-360
  
• Paul W. Goldberg, Mark Jerrum:
  Bounding the Vapnik-Chervonenkis Dimension of Concept Classes Parameterized by Real Numbers. 361-369
  
• B. K. Natarajan:
  Occam's Razor for Functions. 370-376
  
• Eiji Takimoto, Akira Maruoka:
  Conservativeness and Monotonicity for Learning Algorithms. 377-383
  
• György Turán:
  Lower Bounds for PAC Learning with Queries. 384-391
  
• Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger:
  On the Complexity of Function Learning. 392-401
  
• Hans-Ulrich Simon:
  General Bounds on the Number of Examples Needed for Learning Probabilistic Concepts. 402-411
  
• Nick Littlestone, Philip M. Long:
  On-Line Learning with Linear Loss Constraints. 412-421
  
• Naoki Abe, Jun-ichi Takeuchi:
  The "lob-pass" Problem and an On-line Learning Model of Rational Choice. 422-428
  
• Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth:
  Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule. 429-438
  
• S. E. Posner, Sanjeev R. Kulkarni:
  On-Line Learning of Functions of Bounded Variation under Various Sampling Schemes. 439-445
  
• Yoshiyuki Kabashima, Shigeru Shinomoto:
  Acceleration of Learning in Binary Choice Problems. 446-452
  
• Sally A. Goldman, Manfred K. Warmuth:
  Learning Binary Relations Using Weighted Majority Voting. 453-462
  

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