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John Langford: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Alina Beygelzimer, John Langford, Bianca Zadrozny
    Weighted One-Against-All. [Citation Graph (0, 0)][DBLP]
    AAAI, 2005, pp:720-725 [Conf]
  2. Jacob Abernethy, John Langford, Manfred K. Warmuth
    Continuous Experts and the Binning Algorithm. [Citation Graph (0, 0)][DBLP]
    COLT, 2006, pp:544-558 [Conf]
  3. Avrim Blum, Adam Kalai, John Langford
    Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. [Citation Graph (0, 0)][DBLP]
    COLT, 1999, pp:203-208 [Conf]
  4. Avrim Blum, John Langford
    PAC-MDL Bounds. [Citation Graph (0, 0)][DBLP]
    COLT, 2003, pp:344-357 [Conf]
  5. Peter Grünwald, John Langford
    Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification. [Citation Graph (0, 0)][DBLP]
    COLT, 2004, pp:331-347 [Conf]
  6. John Langford
    The Cross Validation Problem. [Citation Graph (0, 0)][DBLP]
    COLT, 2005, pp:687-688 [Conf]
  7. John Langford, Alina Beygelzimer
    Sensitive Error Correcting Output Codes. [Citation Graph (0, 0)][DBLP]
    COLT, 2005, pp:158-172 [Conf]
  8. John Langford, Avrim Blum
    Microchoice Bounds and Self Bounding Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    COLT, 1999, pp:209-214 [Conf]
  9. John Langford, David A. McAllester
    Computable Shell Decomposition Bounds. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:25-34 [Conf]
  10. Nicholas J. Hopper, John Langford, Luis von Ahn
    Provably Secure Steganography. [Citation Graph (0, 0)][DBLP]
    CRYPTO, 2002, pp:77-92 [Conf]
  11. Avrim Blum, John Langford
    Probabilistic Planning in the Graphplan Framework. [Citation Graph (0, 0)][DBLP]
    ECP, 1999, pp:319-332 [Conf]
  12. Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford
    CAPTCHA: Using Hard AI Problems for Security. [Citation Graph (0, 0)][DBLP]
    EUROCRYPT, 2003, pp:294-311 [Conf]
  13. Avrim Blum, Carl Burch, John Langford
    On Learning Monotone Boolean Functions. [Citation Graph (0, 0)][DBLP]
    FOCS, 1998, pp:408-415 [Conf]
  14. Bianca Zadrozny, John Langford, Naoki Abe
    Cost-Sensitive Learning by Cost-Proportionate Example Weighting. [Citation Graph (0, 0)][DBLP]
    ICDM, 2003, pp:435-0 [Conf]
  15. Maria-Florina Balcan, Alina Beygelzimer, John Langford
    Agnostic active learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:65-72 [Conf]
  16. Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford, Bianca Zadrozny
    Error limiting reductions between classification tasks. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:49-56 [Conf]
  17. Alina Beygelzimer, Sham Kakade, John Langford
    Cover trees for nearest neighbor. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:97-104 [Conf]
  18. Matti Kääriäinen, John Langford
    A comparison of tight generalization error bounds. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:409-416 [Conf]
  19. Sham Kakade, Michael J. Kearns, John Langford
    Exploration in Metric State Spaces. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:306-312 [Conf]
  20. Sham Kakade, John Langford
    Approximately Optimal Approximate Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:267-274 [Conf]
  21. John Langford
    Combining Trainig Set and Test Set Bounds. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:331-338 [Conf]
  22. John Langford, Matthias Seeger, Nimrod Megiddo
    An Improved Predictive Accuracy Bound for Averaging Classifiers. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:290-297 [Conf]
  23. John Langford, Bianca Zadrozny
    Relating reinforcement learning performance to classification performance. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:473-480 [Conf]
  24. John Langford, Martin Zinkevich, Sham Kakade
    Competitive Analysis of the Explore/Exploit Tradeoff. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:339-346 [Conf]
  25. Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum
    FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:703-710 [Conf]
  26. Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman
    PAC model-free reinforcement learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:881-888 [Conf]
  27. Sebastian Thrun, John Langford, Dieter Fox
    Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:415-424 [Conf]
  28. Naoki Abe, Bianca Zadrozny, John Langford
    An iterative method for multi-class cost-sensitive learning. [Citation Graph (0, 0)][DBLP]
    KDD, 2004, pp:3-11 [Conf]
  29. Naoki Abe, Bianca Zadrozny, John Langford
    Outlier detection by active learning. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:504-509 [Conf]
  30. Arindam Banerjee, John Langford
    An objective evaluation criterion for clustering. [Citation Graph (0, 0)][DBLP]
    KDD, 2004, pp:515-520 [Conf]
  31. John Langford, Rich Caruana
    (Not) Bounding the True Error. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:809-816 [Conf]
  32. John Langford, John Shawe-Taylor
    PAC-Bayes & Margins. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:423-430 [Conf]
  33. Sebastian Thrun, John Langford, Vandi Verma
    Risk Sensitive Particle Filters. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:961-968 [Conf]
  34. Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz
    Correlated equilibria in graphical games. [Citation Graph (0, 0)][DBLP]
    ACM Conference on Electronic Commerce, 2003, pp:42-47 [Conf]
  35. Luis von Ahn, Nicholas J. Hopper, John Langford
    Covert two-party computation. [Citation Graph (0, 0)][DBLP]
    STOC, 2005, pp:513-522 [Conf]
  36. Luis von Ahn, Manuel Blum, John Langford
    Telling humans and computers apart automatically. [Citation Graph (0, 0)][DBLP]
    Commun. ACM, 2004, v:47, n:2, pp:56-60 [Journal]
  37. Peter Grünwald, John Langford
    Suboptimal behaviour of Bayes and MDL in classification under misspecification [Citation Graph (0, 0)][DBLP]
    CoRR, 2004, v:0, n:, pp:- [Journal]
  38. Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford
    Reductions Between Classification Tasks [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2004, v:, n:077, pp:- [Journal]
  39. John Langford, David A. McAllester
    Computable Shell Decomposition Bounds. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:529-547 [Journal]
  40. John Langford
    Tutorial on Practical Prediction Theory for Classification. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:273-306 [Journal]
  41. John Langford, Avrim Blum
    Microchoice Bounds and Self Bounding Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:51, n:2, pp:165-179 [Journal]
  42. Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin
    Robust Reductions from Ranking to Classification. [Citation Graph (0, 0)][DBLP]
    COLT, 2007, pp:604-619 [Conf]
  43. John Langford, Roberto Oliveira, Bianca Zadrozny
    Predicting Conditional Quantiles via Reduction to Classification. [Citation Graph (0, 0)][DBLP]
    UAI, 2006, pp:- [Conf]
  44. Peter Grünwald, John Langford
    Suboptimal behavior of Bayes and MDL in classification under misspecification. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2007, v:66, n:2-3, pp:119-149 [Journal]

  45. Error-Correcting Tournaments. [Citation Graph (, )][DBLP]

  46. Exploration scavenging. [Citation Graph (, )][DBLP]

  47. Tutorial summary: Reductions in machine learning. [Citation Graph (, )][DBLP]

  48. Learning nonlinear dynamic models. [Citation Graph (, )][DBLP]

  49. Tutorial summary: Active learning. [Citation Graph (, )][DBLP]

  50. Importance weighted active learning. [Citation Graph (, )][DBLP]

  51. Feature hashing for large scale multitask learning. [Citation Graph (, )][DBLP]

  52. The offset tree for learning with partial labels. [Citation Graph (, )][DBLP]

  53. The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. [Citation Graph (, )][DBLP]

  54. Sparse Online Learning via Truncated Gradient. [Citation Graph (, )][DBLP]

  55. Predictive Indexing for Fast Search. [Citation Graph (, )][DBLP]

  56. Self-financed wagering mechanisms for forecasting. [Citation Graph (, )][DBLP]

  57. Maintaining Equilibria During Exploration in Sponsored Search Auctions. [Citation Graph (, )][DBLP]

  58. A contextual-bandit approach to personalized news article recommendation. [Citation Graph (, )][DBLP]

  59. Sparse Online Learning via Truncated Gradient [Citation Graph (, )][DBLP]

  60. The Offset Tree for Learning with Partial Labels [Citation Graph (, )][DBLP]

  61. Importance Weighted Active Learning [Citation Graph (, )][DBLP]

  62. Multi-Label Prediction via Compressed Sensing [Citation Graph (, )][DBLP]

  63. Feature Hashing for Large Scale Multitask Learning [Citation Graph (, )][DBLP]

  64. Error-Correcting Tournaments [Citation Graph (, )][DBLP]

  65. Conditional Probability Tree Estimation Analysis and Algorithms [Citation Graph (, )][DBLP]

  66. Learning Nonlinear Dynamic Models [Citation Graph (, )][DBLP]

  67. Search-based Structured Prediction [Citation Graph (, )][DBLP]

  68. An Optimal High Probability Algorithm for the Contextual Bandit Problem [Citation Graph (, )][DBLP]

  69. Learning from Logged Implicit Exploration Data [Citation Graph (, )][DBLP]

  70. A Contextual-Bandit Approach to Personalized News Article Recommendation [Citation Graph (, )][DBLP]

  71. An Unbiased, Data-Driven, Offline Evaluation Method of Contextual Bandit Algorithms [Citation Graph (, )][DBLP]

  72. Agnostic Active Learning Without Constraints [Citation Graph (, )][DBLP]

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