Learning Halfspaces with Malicious Noise, Adam R. Klivans, Philip M. Long, Rocco A. Servedio, JMLR (2010) (to appear).
Baum's algorithm learns intersections of halfspaces with respect to log-concave distributions, Adam R. Klivans, Philip M. Long, Alex K. Tang, RANDOM, 2009.
Linear classifiers are nearly optimal when hidden variables have diverse effects, Nader H. Bshouty, Philip M. Long, COLT, 2009.
Random classification noise defeats all convex potential boosters, Philip M. Long, Rocco A. Servedio, Machine Learning, 2009.
Using the Doubling Dimension to Analyze the Generalization of Learning Algorithms, Nader H. Bshouty, Yi Li, Philip M. Long, JCSS (2009).
Adaptive Martingale Boosting, Philip M. Long, Rocco A. Servedio, NIPS, 2008.
Boosting the area under the ROC curve, Philip M. Long, Rocco A. Servedio, NIPS, 2007.
Discriminative learning can succeed where generative learning fails, Philip M. Long, Rocco A. Servedio, Hans Ulrich Simon, Information Processing Letters, vol. 103(4) (2007), pp. 131-135.
One-pass boosting, Zafer Barutcuoglu, Philip M. Long, Rocco A. Servedio, NIPS, 2007.
Online learning of multiple tasks with a shared loss, Ofer Dekel, Philip M. Long, Yoram Singer, JMLR, vol. 8 (2007), pp. 2233-2264.
Attribute-efficient learning of linear threshold functions under unconcentrated distributions, Philip M. Long, Rocco A. Servedio, NIPS, 2006.
Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis, 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, IAAI, 2006.
Martingale Boosting, Philip M. Long, Rocco A. Servedio, COLT, 2005.
Performance guarantees for hierarchical clustering, Sanjoy Dasgupta, Philip M. Long, Journal of Computer and System Sciences, vol. 70(4) (2005), pp. 555-569.
Unsupervised evidence integration, Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio, ICML, 2005.
Mistake bounds for maximum entropy discrimination., Philip M. Long, Xinyu Wu, NIPS, 2004.
An upper bound on the sample complexity of PAC learning halfspaces with respect to the uniform distribution, Philip M. Long, Information Processing Letters, vol. 87(5) (2003), pp. 229-234.
Boosting and microarray data, Philip M. Long, Vinsensius B. Vega, Machine Learning, vol. 52(1) (2003), pp. 31-44.
Boosting with diverse base classifiers, Sanjoy Dasgupta, Philip M. Long, COLT, 2003.
On the difficulty of approximately maximizing agreements, Shai Ben-David, Nadav Eiron, Philip M. Long, J. Comput. Syst. Sci., vol. 66 (2003), pp. 496-514.
Minimum majority classification and boosting, Philip M. Long, AAAI, 2002.
The Relaxed Online Maximum Margin Algorithm, Yi Li, Philip M. Long, Machine Learning, vol. 46 (2002), pp. 361-387.
Improved Bounds on the Sample Complexity of Learning, Yi Li, Philip M. Long, Aravind Srinivasan, J. Comput. Syst. Sci., vol. 62 (2001), pp. 516-527.
On agnostic learning with 0,*,1-valued and real-valued hypotheses, Philip M. Long, COLT, 2001.
The one-inclusion graph algorithm is near-optimal for the prediction model of learning, Yi Li, Philip M. Long, Aravind Srinivasan, IEEE Transactions on Information Theory, vol. 47 (2001), pp. 1257-1261.
Using the pseudo-dimension to analyze approximation algorithms for integer programming., Philip M. Long, WADS, 2001.
Apple Tasting, David P. Helmbold, Nicholas Littlestone, Philip M. Long, Information and Computation, vol. 161(2) (2000), pp. 85-139.
Improved bounds about on-line learning of smooth functions of a single variable, Philip M. Long, Theoretical Computer Science, vol. 241(1-2) (2000), pp. 25-35.
On the Difficulty of Approximately Maximizing Agreements, Shai Ben-David, Nadav Eiron, Philip M. Long, COLT, 2000, pp. 266-274.
Adaptive disk spindown via optimal rent-to-buy in probabilistic environments, P. Krishnan, Philip M. Long, Jeffrey Scott Vitter, Algorithmica, vol. 23(1) (1999), pp. 31-56.
Dictionary selection using partial matching, Dzung T. Hoang, Philip M. Long, Jeffrey Scott Vitter, Information Sciences, vol. 119(1-2) (1999), pp. 57-72.
The complexity of learning according to two models of a drifting environment, Philip M. Long, Machine Learning, vol. 37(3) (1999), pp. 337-354.
Prediction, learning, uniform convergence, and scale-sensitive dimensions., Peter L. Bartlett, Philip M. Long, Journal of Computer and System Sciences, vol. 56(2) (1998), pp. 174-190.
Fat shattering and the learnability of real-valued functions, Peter L. Bartlett, Philip M. Long, Robert C. Williamson, Journal of Computer and System Sciences, vol. 52(3) (1996), pp. 434-452.
A generalization of Sauer's Lemma, David Haussler, Philip M. Long, Journal of Combinatorial Theory, Series A, vol. 71(2) (1995), pp. 219-240.
On the sample complexity of PAC learning halfspaces against the uniform distribution, Philip M. Long, IEEE Transactions on Neural Networks, vol. 6(6) (1995), pp. 1556-1559.
On-line learning of linear functions, Nicholas Littlestone, Philip M. Long, Manfred K. Warmuth, Computational Complexity, vol. 5 (1995), pp. 1-23.