3. COLT 1990: Rochester, NY, USA

• Rusins Freivalds:
  Inductive Inference of Minimal Programs. 3-22
  
• Thomas R. Hancock:
  Identifying µ-Formula Decision Trees with Queries. 23-37
  
• Vijay Raghavan, Stephen R. Schach:
  Learning Switch Configurations. 38-51
  
• Naoki Abe, Manfred K. Warmuth:
  On the Computational Complexity of Approximating Distributions by Probabilistic Automata. 52-66
  
• Kenji Yamanishi:
  A Learning Criterion for Stochastic Rules. 67-81
  
• Ker-I Ko:
  On the Complexity of Learning Minimum Time-Bounded Turing Machines. 82-96
  
• Takeshi Shinohara:
  Inductive Inference from Positive Data is Powerful. 97-110
  
• Keith Wright:
  Inductive Identification of Pattern Languages Restricted Substitutions. 111-121
  
• Robert E. Schapire:
  Pattern Languages are not Learnable. 122-129
  
• Paul Fischer, Hans-Ulrich Simon:
  On Learning Ring-Sum-Expansions. 130-143
  
• Avrim Blum, Mona Singh:
  Learning Functions of k Terms. 144-153
  
• Bonnie Eisenberg, Ronald L. Rivest:
  On the Sample Complexity of PAC-Learning Using Random and Chosen Examples. 154-162
  
• Sanjay Jain, Arun Sharma:
  Finite Learning by a ``Team''. 163-177
  
• Efim B. Kinber:
  Some Problems of Learning with an Oracle. 178-186
  
• Daniel N. Osherson, Michael Stob, Scott Weinstein:
  A Mechanical Method of Successful Scientific Inquiry. 187-201
  
• Yoav Freund:
  Boosting a Weak Learning Algorithm by Majority. 202-216
  
• Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
  On the Sample Complexity of Weak Learning. 217-231
  
• Shai Ben-David, Alon Itai, Eyal Kushilevitz:
  Learning by Distances. 232-245
  
• Martin Anthony, Norman Biggs, John Shawe-Taylor:
  The Learnability of Formal Concepts. 246-257
  
• Eric B. Baum:
  Polynomial Time Algorithms for Learning Neural Nets. 258-272
  
• Philip M. Long, Manfred K. Warmuth:
  Composite Geometric Concepts and Polynomial Predictability. 273-287
  
• David P. Helmbold, Robert H. Sloan, Manfred K. Warmuth:
  Learning Integer Lattices. 288-302
  
• Hans-Ulrich Simon:
  On the Number of Examples and Stages Needed for Learning Decision Trees. 303-313
  
• Karsten A. Verbeurgt:
  Learning DNF Under the Uniform Distribution in Quasi-Polynomial Time. 314-326
  
• Efim B. Kinber, William I. Gasarch, Thomas Zeugmann, Mark G. Pleszkoch, Carl H. Smith:
  Learning Via Queries With Teams and Anomilies. 327-337
  
• William I. Gasarch, Mark G. Pleszkoch, Robert Solovay:
  Learning Via Queries in [+, <]. 338-351
  
• Pekka Orponen, Russell Greiner:
  On the Sample Complexity of Finding Good Search Strategies. 352-358
  
• Javed A. Aslam, Ronald L. Rivest:
  Inferring Graphs from Walks. 359-370
  
• V. G. Vovk:
  Aggregating Strategies. 371-386
  
• Dana Angluin, Michael Frazier, Leonard Pitt:
  Learning Conjunctions of Horn Clauses (Abstract). 387
  
• Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
  Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). 388
  
• Michael J. Kearns, Robert E. Schapire:
  Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). 389
  
• Ramamohan Paturi, Michael E. Saks:
  On Threshold Circuits for Parity (Abstract). 390
  
• Wolfgang Maass, György Turán:
  On the Complexity of Learning from Counterexamples and Membership Queries (abstract). 391
  
• Mark A. Fulk:
  Robust Separations in Inductive Inference (Abstract). 392
  
• Avrim Blum:
  Separating PAC and Mistake-Bound Learning Models Over the Boolean Domain (Abstract). 393