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Philip M. Long: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Philip Gross, Albert Boulanger, Marta Arias, David L. Waltz, Philip M. Long, Charles Lawson, Roger Anderson, Matthew Koenig, Mark Mastrocinque, William Fairechio, John A. Johnson, Serena Lee, Frank Doherty, Arthur Kressner
    Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis. [Citation Graph (0, 0)][DBLP]
    AAAI, 2006, pp:- [Conf]
  2. Philip M. Long
    Minimum Majority Classification and Boosting. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2002, pp:181-186 [Conf]
  3. José L. Balcázar, Philip M. Long, Frank Stephan
    Editors' Introduction. [Citation Graph (0, 0)][DBLP]
    ALT, 2006, pp:1-9 [Conf]
  4. Philip M. Long
    Improved Bounds about On-line Learning of Smooth Functions of a Single Variable. [Citation Graph (0, 0)][DBLP]
    ALT, 1996, pp:26-36 [Conf]
  5. Rakesh D. Barve, Philip M. Long
    On the Complexity of Learning from Drifting Distributions. [Citation Graph (0, 0)][DBLP]
    COLT, 1996, pp:122-130 [Conf]
  6. Shai Ben-David, Nicolò Cesa-Bianchi, Philip M. Long
    Characterizations of Learnability for Classes of {O, ..., n}-Valued Functions. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:333-340 [Conf]
  7. Shai Ben-David, Nadav Eiron, Philip M. Long
    On the Difficulty of Approximately Maximizing Agreements. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:266-274 [Conf]
  8. Shai Ben-David, Philip M. Long, Yishay Mansour
    Agnostic Boosting. [Citation Graph (0, 0)][DBLP]
    COLT/EuroCOLT, 2001, pp:507-516 [Conf]
  9. Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth
    Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule. [Citation Graph (0, 0)][DBLP]
    COLT, 1993, pp:429-438 [Conf]
  10. Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
    On the Complexity of Function Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1993, pp:392-401 [Conf]
  11. Peter L. Bartlett, Philip M. Long
    More Theorems about Scale-sensitive Dimensions and Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1995, pp:392-401 [Conf]
  12. Peter L. Bartlett, Philip M. Long, Robert C. Williamson
    Fat-Shattering and the Learnability of Real-Valued Functions. [Citation Graph (0, 0)][DBLP]
    COLT, 1994, pp:299-310 [Conf]
  13. Sanjoy Dasgupta, Philip M. Long
    Boosting with Diverse Base Classifiers. [Citation Graph (0, 0)][DBLP]
    COLT, 2003, pp:273-287 [Conf]
  14. Ofer Dekel, Philip M. Long, Yoram Singer
    Online Multitask Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 2006, pp:453-467 [Conf]
  15. David P. Helmbold, Philip M. Long
    Tracking Drifting Concepts Using Random Examples. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:13-23 [Conf]
  16. Don Kimber, Philip M. Long
    The Learning Complexity of Smooth Functions of a Single Variable. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:153-159 [Conf]
  17. Wee Sun Lee, Philip M. Long
    A Theoretical Analysis of Query Selection for Collaborative Filtering. [Citation Graph (0, 0)][DBLP]
    COLT/EuroCOLT, 2001, pp:517-528 [Conf]
  18. Nick Littlestone, Philip M. Long
    On-Line Learning with Linear Loss Constraints. [Citation Graph (0, 0)][DBLP]
    COLT, 1993, pp:412-421 [Conf]
  19. Philip M. Long
    On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses. [Citation Graph (0, 0)][DBLP]
    COLT/EuroCOLT, 2001, pp:289-302 [Conf]
  20. Philip M. Long
    On-line Evaluation and Prediction using Linear Functions. [Citation Graph (0, 0)][DBLP]
    COLT, 1997, pp:21-31 [Conf]
  21. Philip M. Long
    The complexity of learning according to two models of a drifting environment. [Citation Graph (0, 0)][DBLP]
    COLT, 1998, pp:116-125 [Conf]
  22. Philip M. Long
    On the Sample Complexity of Learning Functions with Bounded Variation. [Citation Graph (0, 0)][DBLP]
    COLT, 1998, pp:126-133 [Conf]
  23. Philip M. Long, Rocco A. Servedio
    Martingale Boosting. [Citation Graph (0, 0)][DBLP]
    COLT, 2005, pp:79-94 [Conf]
  24. Philip M. Long, Rocco A. Servedio
    Discriminative Learning Can Succeed Where Generative Learning Fails. [Citation Graph (0, 0)][DBLP]
    COLT, 2006, pp:319-334 [Conf]
  25. Philip M. Long, Lei Tan
    PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples. [Citation Graph (0, 0)][DBLP]
    COLT, 1996, pp:228-234 [Conf]
  26. Philip M. Long, Manfred K. Warmuth
    Composite Geometric Concepts and Polynomial Predictability. [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:273-287 [Conf]
  27. Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter
    Explicit Bit Minimization for Motion-Compensated Video Coding. [Citation Graph (0, 0)][DBLP]
    Data Compression Conference, 1994, pp:175-184 [Conf]
  28. Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter
    Multiple-Dictionary Coding Using Partial Matching. [Citation Graph (0, 0)][DBLP]
    Data Compression Conference, 1995, pp:272-281 [Conf]
  29. Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter
    Efficient Cost Measures for Motion Compensation at Low Bit Rates (Extended Abstract). [Citation Graph (0, 0)][DBLP]
    Data Compression Conference, 1996, pp:102-111 [Conf]
  30. Philip M. Long, Apostol Natsev, Jeffrey Scott Vitter
    Text Compression Via Alphabet Re-Representation. [Citation Graph (0, 0)][DBLP]
    Data Compression Conference, 1997, pp:161-170 [Conf]
  31. David P. Helmbold, Nick Littlestone, Philip M. Long
    Apple Tasting and Nearly One-Sided Learning [Citation Graph (0, 0)][DBLP]
    FOCS, 1992, pp:493-502 [Conf]
  32. Naoki Abe, Philip M. Long
    Associative Reinforcement Learning using Linear Probabilistic Concepts. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:3-11 [Conf]
  33. P. Krishnan, Philip M. Long, Jeffrey Scott Vitter
    Learning to Make Rent-to-Buy Decisions with Systems Applications. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:233-330 [Conf]
  34. Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio
    Unsupervised evidence integration. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:521-528 [Conf]
  35. Yi Li, Philip M. Long
    The Relaxed Online Maximum Margin Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:498-504 [Conf]
  36. Philip M. Long, Xinyu Wu
    Mistake Bounds for Maximum Entropy Discrimination. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  37. Yi Li, Philip M. Long, Aravind Srinivasan
    Improved bounds on the sample complexity of learning. [Citation Graph (0, 0)][DBLP]
    SODA, 2000, pp:309-318 [Conf]
  38. Peter Auer, Philip M. Long
    Simulating access to hidden information while learning. [Citation Graph (0, 0)][DBLP]
    STOC, 1994, pp:263-272 [Conf]
  39. Peter Auer, Philip M. Long, Aravind Srinivasan
    Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets. [Citation Graph (0, 0)][DBLP]
    STOC, 1997, pp:314-323 [Conf]
  40. Nick Littlestone, Philip M. Long, Manfred K. Warmuth
    On-Line Learning of Linear Functions [Citation Graph (0, 0)][DBLP]
    STOC, 1991, pp:465-475 [Conf]
  41. Philip M. Long
    Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming. [Citation Graph (0, 0)][DBLP]
    WADS, 2001, pp:26-37 [Conf]
  42. Naoki Abe, Alan W. Biermann, Philip M. Long
    Reinforcement Learning with Immediate Rewards and Linear Hypotheses. [Citation Graph (0, 0)][DBLP]
    Algorithmica, 2003, v:37, n:4, pp:263-293 [Journal]
  43. P. Krishnan, Philip M. Long, Jeffrey Scott Vitter
    Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments. [Citation Graph (0, 0)][DBLP]
    Algorithmica, 1999, v:23, n:1, pp:31-56 [Journal]
  44. Nick Littlestone, Philip M. Long, Manfred K. Warmuth
    On-line Learning of Linear Functions. [Citation Graph (0, 0)][DBLP]
    Computational Complexity, 1995, v:5, n:1, pp:1-23 [Journal]
  45. Peter Auer, Philip M. Long
    Simulating Access to Hidden Information while Learning [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:67, pp:- [Journal]
  46. Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
    On the Complexity of Function Learning [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:50, pp:- [Journal]
  47. Peter Auer, Philip M. Long, Aravind Srinivasan
    Approximating Hyper-Rectangles: Learning and Pseudo-random Sets [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:72, pp:- [Journal]
  48. Rakesh D. Barve, Philip M. Long
    On the Complexity of Learning from Drifting Distributions. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1997, v:138, n:2, pp:170-193 [Journal]
  49. David P. Helmbold, Nick Littlestone, Philip M. Long
    Apple Tasting. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 2000, v:161, n:2, pp:85-139 [Journal]
  50. David P. Helmbold, Nick Littlestone, Philip M. Long
    On-Line Learning with Linear Loss Constraints. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 2000, v:161, n:2, pp:140-171 [Journal]
  51. Philip M. Long
    Efficient algorithms for learning functions with bounded variation. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 2004, v:188, n:1, pp:99-115 [Journal]
  52. Philip M. Long, Manfred K. Warmuth
    Composite Geometric Concepts and Polynomial Predictability [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1994, v:113, n:2, pp:230-252 [Journal]
  53. Philip M. Long
    An upper bound on the sample complexity of PAC-learning halfspaces with respect to the uniform distribution. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Lett., 2003, v:87, n:5, pp:229-234 [Journal]
  54. Philip M. Long
    Halfspace Learning, Linear Programming, and Nonmalicious Distributions. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Lett., 1994, v:51, n:5, pp:245-250 [Journal]
  55. Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter
    Dictionary Selection Using Partial Matching. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 1999, v:119, n:1-2, pp:57-72 [Journal]
  56. Peter Auer, Philip M. Long, Aravind Srinivasan
    Approximating Hyper-Rectangles: Learning and Pseudorandom Sets. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1998, v:57, n:3, pp:376-388 [Journal]
  57. Peter L. Bartlett, Philip M. Long
    Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1998, v:56, n:2, pp:174-190 [Journal]
  58. Peter L. Bartlett, Philip M. Long, Robert C. Williamson
    Fat-Shattering and the Learnability of Real-Valued Functions. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1996, v:52, n:3, pp:434-452 [Journal]
  59. Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler, Philip M. Long
    Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1995, v:50, n:1, pp:74-86 [Journal]
  60. Shai Ben-David, Nadav Eiron, Philip M. Long
    On the difficulty of approximately maximizing agreements. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2003, v:66, n:3, pp:496-514 [Journal]
  61. Sanjoy Dasgupta, Philip M. Long
    Performance guarantees for hierarchical clustering. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2005, v:70, n:4, pp:555-569 [Journal]
  62. Yi Li, Philip M. Long, Aravind Srinivasan
    Improved Bounds on the Sample Complexity of Learning. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2001, v:62, n:3, pp:516-527 [Journal]
  63. David Haussler, Philip M. Long
    A Generalization of Sauer's Lemma. [Citation Graph (0, 0)][DBLP]
    J. Comb. Theory, Ser. A, 1995, v:71, n:2, pp:219-240 [Journal]
  64. Peter Auer, Philip M. Long
    Structural Results About On-line Learning Models With and Without Queries. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:36, n:3, pp:147-181 [Journal]
  65. Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger
    On the Complexity of Function Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1995, v:18, n:2-3, pp:187-230 [Journal]
  66. Sanjoy Dasgupta, Wee Sun Lee, Philip M. Long
    A Theoretical Analysis of Query Selection for Collaborative Filtering. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:51, n:3, pp:283-298 [Journal]
  67. David P. Helmbold, Philip M. Long
    Tracking Drifting Concepts By Minimizing Disagreements. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:14, n:1, pp:27-45 [Journal]
  68. Yi Li, Philip M. Long
    The Relaxed Online Maximum Margin Algorithm. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:46, n:1-3, pp:361-387 [Journal]
  69. Philip M. Long
    Guest Editor's Introduction. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:27, n:1, pp:5- [Journal]
  70. Philip M. Long
    The Complexity of Learning According to Two Models of a Drifting Environment. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:37, n:3, pp:337-354 [Journal]
  71. Philip M. Long, Vinsensius Berlian Vega SN
    Boosting and Microarray Data. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:52, n:1-2, pp:31-44 [Journal]
  72. Philip M. Long, Lei Tan
    PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1998, v:30, n:1, pp:7-21 [Journal]
  73. Philip M. Long, Apostol Natsev, Jeffrey Scott Vitter
    Text compression via alphabet re-representation. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1999, v:12, n:4-5, pp:755-765 [Journal]
  74. Don Kimber, Philip M. Long
    On-Line Learning of Smooth Functions of a Single Variable. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 1995, v:148, n:1, pp:141-156 [Journal]
  75. Philip M. Long
    Improved bounds about on-line learning of smooth-functions of a single variable. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2000, v:241, n:1-2, pp:25-35 [Journal]
  76. Yi Li, Philip M. Long, Aravind Srinivasan
    The one-inclusion graph algorithm is near-optimal for the prediction model of learning. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2001, v:47, n:3, pp:1257-1261 [Journal]
  77. Philip M. Long, Rocco A. Servedio
    Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:921-928 [Conf]
  78. Yi Li, Philip M. Long
    Learnability and the doubling dimension. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:889-896 [Conf]
  79. Philip M. Long, Rocco A. Servedio, Hans-Ulrich Simon
    Discriminative learning can succeed where generative learning fails. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Lett., 2007, v:103, n:4, pp:131-135 [Journal]

  80. Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions. [Citation Graph (, )][DBLP]


  81. Learning Halfspaces with Malicious Noise. [Citation Graph (, )][DBLP]


  82. Random classification noise defeats all convex potential boosters. [Citation Graph (, )][DBLP]


  83. Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering. [Citation Graph (, )][DBLP]


  84. Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate. [Citation Graph (, )][DBLP]


  85. One-Pass Boosting. [Citation Graph (, )][DBLP]


  86. Boosting the Area under the ROC Curve. [Citation Graph (, )][DBLP]


  87. Adaptive Martingale Boosting. [Citation Graph (, )][DBLP]


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