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Computational Learning Theory

14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001 Amsterdam, The Netherlands, July 16–19, 2001 Proceedings

  • David Helmbold
  • Bob Williamson
Conference proceedings COLT 2001

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2111)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2111)

Table of contents

  1. Front Matter
    Pages I-IX
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  2. How Many Queries Are Needed to Learn One Bit of Information?
    Hans Ulrich Simon
    Pages 1-13
  3. Radial Basis Function Neural Networks Have Superlinear VC Dimension
    Michael Schmitt
    Pages 14-30
  4. Tracking a Small Set of Experts by Mixing Past Posteriors
    Olivier Bousquet, Manfred K. Warmuth
    Pages 31-47
  5. Potential-Based Algorithms in Online Prediction and Game Theory
    Nicolò Cesa-Bianchi, Gábor Lugosi
    Pages 48-64
  6. A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning
    Tong Zhang
    Pages 65-81
  7. Efficiently Approximating Weighted Sums with Exponentially Many Terms
    Deepak Chawla, Lin Li, Stephen Scott
    Pages 82-98
  8. Ultraconservative Online Algorithms for Multiclass Problems
    Koby Crammer, Yoram Singer
    Pages 99-115
  9. Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required
    Paul W. Goldberg
    Pages 116-127
  10. Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments
    Shie Mannor, Nahum Shimkin
    Pages 128-142
  11. Robust Learning — Rich and Poor
    John Case, Sanjay Jain, Frank Stephan, Rolf Wiehagen
    Pages 143-159
  12. On the Synthesis of Strategies Identifying Recursive Functions
    Sandra Zilles
    Pages 160-176
  13. Intrinsic Complexity of Learning Geometrical Concepts from Positive Data
    Sanjay Jain, Efim Kinber
    Pages 177-193
  14. Toward a Computational Theory of Data Acquisition and Truthing
    David G. Stork
    Pages 194-207
  15. Discrete Prediction Games with Arbitrary Feedback and Loss (Extended Abstract)
    Antonio Piccolboni, Christian Schindelhauer
    Pages 208-223
  16. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
    Peter L. Bartlett, Shahar Mendelson
    Pages 224-240
  17. Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights
    Vladimir Koltchinskii, Dmitriy Panchenko, Fernando Lozano
    Pages 241-255
  18. Geometric Methods in the Analysis of Glivenko-Cantelli Classes
    Shahar Mendelson
    Pages 256-272
  19. Learning Relatively Small Classes
    Shahar Mendelson
    Pages 273-288
  20. On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses
    Philip M. Long
    Pages 289-302
  21. When Can Two Unsupervised Learners Achieve PAC Separation?
    Paul W. Goldberg
    Pages 303-319
  22. Strong Entropy Concentration, Game Theory, and Algorithmic Randomness
    Peter Grünwald
    Pages 320-336
  23. Pattern Recognition and Density Estimation under the General i.i.d. Assumption
    Ilia Nouretdinov, Volodya Vovk, Michael Vyugin, Alex Gammerman
    Pages 337-353
  24. A General Dimension for Exact Learning
    José L. Balcázar, Jorge Castro, David Guijarro
    Pages 354-367
  25. Data-Dependent Margin-Based Generalization Bounds for Classification
    Balázs Kégl, Tamás Linder, Gábor Lugosi
    Pages 368-384
  26. Limitations of Learning via Embeddings in Euclidean Half-Spaces
    Shai Ben-David, Nadav Eiron, Hans Ulrich Simon
    Pages 385-401
  27. Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces
    Jürgen Forster, Niels Schmitt, Hans Ulrich Simon
    Pages 402-415
  28. A Generalized Representer Theorem
    Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola
    Pages 416-426
  29. A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning
    Tong Zhang
    Pages 427-443
  30. Learning Additive Models Online with Fast Evaluating Kernels
    Mark Herbster
    Pages 444-460
  31. Geometric Bounds for Generalization in Boosting
    Shie Mannor, Ron Meir
    Pages 461-472
  32. Smooth Boosting and Learning with Malicious Noise
    Rocco A. Servedio
    Pages 473-489
  33. On Boosting with Optimal Poly-Bounded Distributions
    Nader H. Bshouty, Dmitry Gavinsky
    Pages 490-506
  34. Agnostic Boosting
    Shai Ben-David, Philip M. Long, Yishay Mansour
    Pages 507-516
  35. A Theoretical Analysis of Query Selection for Collaborative Filtering
    Wee Sun Lee, Philip M. Long
    Pages 517-528
  36. On Using Extended Statistical Queries to Avoid Membership Queries
    Nader H. Bshouty, Vitaly Feldman
    Pages 529-545
  37. Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries
    Nader H. Bshouty, Nadav Eiron
    Pages 546-557
  38. On Learning Monotone DNF under Product Distributions
    Rocco A. Servedio
    Pages 558-573
  39. Learning Regular Sets with an Incomplete Membership Oracle
    Nader Bshouty, Avi Owshanko
    Pages 574-588
  40. Learning Rates for Q-Learning
    Eyal Even-Dar, Yishay Mansour
    Pages 589-604
  41. Optimizing Average Reward Using Discounted Rewards
    Sham Kakade
    Pages 605-615
  42. Bounds on Sample Size for Policy Evaluation in Markov Environments
    Leonid Peshkin, Sayan Mukherjee
    Pages 616-629
  43. Back Matter
    Pages 631-631
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About these proceedings

Keywords

Algorithmic Learning Boosting Classification Computational Learning Computational Learning Theory Data Mining Game Theory Inference Q-Learning algorithms cognition complexity kernel method learning theory optimization

Editors and affiliations

  • David Helmbold
    • 1
  • Bob Williamson
    • 2
  1. 1.School of Engineering, Department of Computer ScienceUniversity of California, Santa CruzSanta CruzUSA
  2. 2.Research School of Information Sciences and Engineering Department of Telecommunications EngineeringAustralian National UniversityCanberraAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-44581-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2001
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-42343-0
  • Online ISBN 978-3-540-44581-4
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site