| 2008 |
|---|
| 86 | EE | Philip M. Long,
Rocco A. Servedio:
Random classification noise defeats all convex potential boosters.
ICML 2008: 608-615 |
| 85 | EE | Philip M. Long,
Rocco A. Servedio:
Adaptive Martingale Boosting.
NIPS 2008: 977-984 |
| 84 | EE | Peter Auer,
Philip M. Long:
Guest editors' introduction: Special issue on learning theory.
J. Comput. Syst. Sci. 74(8): 1227 (2008) |
| 83 | EE | Philip M. Long,
Frank Stephan:
Preface.
Theor. Comput. Sci. 405(3): 207-208 (2008) |
| 2007 |
|---|
| 82 | EE | Philip M. Long,
Rocco A. Servedio:
Boosting the Area under the ROC Curve.
NIPS 2007 |
| 81 | EE | Zafer Barutçuoglu,
Philip M. Long,
Rocco A. Servedio:
One-Pass Boosting.
NIPS 2007 |
| 80 | EE | Philip M. Long,
Rocco A. Servedio,
Hans-Ulrich Simon:
Discriminative learning can succeed where generative learning fails.
Inf. Process. Lett. 103(4): 131-135 (2007) |
| 2006 |
|---|
| 79 | | José L. Balcázar,
Philip M. Long,
Frank Stephan:
Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings
Springer 2006 |
| 78 | | 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.
AAAI 2006 |
| 77 | EE | José L. Balcázar,
Philip M. Long,
Frank Stephan:
Editors' Introduction.
ALT 2006: 1-9 |
| 76 | EE | Philip M. Long,
Rocco A. Servedio:
Discriminative Learning Can Succeed Where Generative Learning Fails.
COLT 2006: 319-334 |
| 75 | EE | Ofer Dekel,
Philip M. Long,
Yoram Singer:
Online Multitask Learning.
COLT 2006: 453-467 |
| 74 | EE | Yi Li,
Philip M. Long:
Learnability and the doubling dimension.
NIPS 2006: 889-896 |
| 73 | EE | Philip M. Long,
Rocco A. Servedio:
Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions.
NIPS 2006: 921-928 |
| 2005 |
|---|
| 72 | EE | Philip M. Long,
Rocco A. Servedio:
Martingale Boosting.
COLT 2005: 79-94 |
| 71 | EE | Philip M. Long,
Vinay Varadan,
Sarah Gilman,
Mark Treshock,
Rocco A. Servedio:
Unsupervised evidence integration.
ICML 2005: 521-528 |
| 70 | EE | Sanjoy Dasgupta,
Philip M. Long:
Performance guarantees for hierarchical clustering.
J. Comput. Syst. Sci. 70(4): 555-569 (2005) |
| 2004 |
|---|
| 69 | EE | Philip M. Long,
Xinyu Wu:
Mistake Bounds for Maximum Entropy Discrimination.
NIPS 2004 |
| 68 | EE | Philip M. Long:
Efficient algorithms for learning functions with bounded variation.
Inf. Comput. 188(1): 99-115 (2004) |
| 2003 |
|---|
| 67 | EE | Sanjoy Dasgupta,
Philip M. Long:
Boosting with Diverse Base Classifiers.
COLT 2003: 273-287 |
| 66 | EE | Naoki Abe,
Alan W. Biermann,
Philip M. Long:
Reinforcement Learning with Immediate Rewards and Linear Hypotheses.
Algorithmica 37(4): 263-293 (2003) |
| 65 | EE | Philip M. Long:
An upper bound on the sample complexity of PAC-learning halfspaces with respect to the uniform distribution.
Inf. Process. Lett. 87(5): 229-234 (2003) |
| 64 | EE | Shai Ben-David,
Nadav Eiron,
Philip M. Long:
On the difficulty of approximately maximizing agreements.
J. Comput. Syst. Sci. 66(3): 496-514 (2003) |
| 63 | EE | Sanjoy Dasgupta,
Wee Sun Lee,
Philip M. Long:
A Theoretical Analysis of Query Selection for Collaborative Filtering.
Machine Learning 51(3): 283-298 (2003) |
| 62 | EE | Philip M. Long,
Vinsensius Berlian Vega SN:
Boosting and Microarray Data.
Machine Learning 52(1-2): 31-44 (2003) |
| 2002 |
|---|
| 61 | | Philip M. Long:
Minimum Majority Classification and Boosting.
AAAI/IAAI 2002: 181-186 |
| 60 | | Yi Li,
Philip M. Long:
The Relaxed Online Maximum Margin Algorithm.
Machine Learning 46(1-3): 361-387 (2002) |
| 2001 |
|---|
| 59 | EE | Philip M. Long:
On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses.
COLT/EuroCOLT 2001: 289-302 |
| 58 | EE | Shai Ben-David,
Philip M. Long,
Yishay Mansour:
Agnostic Boosting.
COLT/EuroCOLT 2001: 507-516 |
| 57 | EE | Wee Sun Lee,
Philip M. Long:
A Theoretical Analysis of Query Selection for Collaborative Filtering.
COLT/EuroCOLT 2001: 517-528 |
| 56 | EE | Philip M. Long:
Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming.
WADS 2001: 26-37 |
| 55 | | Yi Li,
Philip M. Long,
Aravind Srinivasan:
The one-inclusion graph algorithm is near-optimal for the prediction model of learning.
IEEE Transactions on Information Theory 47(3): 1257-1261 (2001) |
| 54 | | Yi Li,
Philip M. Long,
Aravind Srinivasan:
Improved Bounds on the Sample Complexity of Learning.
J. Comput. Syst. Sci. 62(3): 516-527 (2001) |
| 2000 |
|---|
| 53 | | Shai Ben-David,
Nadav Eiron,
Philip M. Long:
On the Difficulty of Approximately Maximizing Agreements.
COLT 2000: 266-274 |
| 52 | EE | Yi Li,
Philip M. Long,
Aravind Srinivasan:
Improved bounds on the sample complexity of learning.
SODA 2000: 309-318 |
| 51 | EE | Peter Auer,
Philip M. Long,
Wolfgang Maass,
Gerhard J. Woeginger:
On the Complexity of Function Learning
Electronic Colloquium on Computational Complexity (ECCC) 7(50): (2000) |
| 50 | EE | Peter Auer,
Philip M. Long:
Simulating Access to Hidden Information while Learning
Electronic Colloquium on Computational Complexity (ECCC) 7(67): (2000) |
| 49 | EE | Peter Auer,
Philip M. Long,
Aravind Srinivasan:
Approximating Hyper-Rectangles: Learning and Pseudo-random Sets
Electronic Colloquium on Computational Complexity (ECCC) 7(72): (2000) |
| 48 | | David P. Helmbold,
Nick Littlestone,
Philip M. Long:
On-Line Learning with Linear Loss Constraints.
Inf. Comput. 161(2): 140-171 (2000) |
| 47 | | David P. Helmbold,
Nick Littlestone,
Philip M. Long:
Apple Tasting.
Inf. Comput. 161(2): 85-139 (2000) |
| 46 | EE | Philip M. Long:
Improved bounds about on-line learning of smooth-functions of a single variable.
Theor. Comput. Sci. 241(1-2): 25-35 (2000) |
| 1999 |
|---|
| 45 | | Naoki Abe,
Philip M. Long:
Associative Reinforcement Learning using Linear Probabilistic Concepts.
ICML 1999: 3-11 |
| 44 | EE | Yi Li,
Philip M. Long:
The Relaxed Online Maximum Margin Algorithm.
NIPS 1999: 498-504 |
| 43 | EE | P. Krishnan,
Philip M. Long,
Jeffrey Scott Vitter:
Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments.
Algorithmica 23(1): 31-56 (1999) |
| 42 | | Dzung T. Hoang,
Philip M. Long,
Jeffrey Scott Vitter:
Dictionary Selection Using Partial Matching.
Inf. Sci. 119(1-2): 57-72 (1999) |
| 41 | | Peter Auer,
Philip M. Long:
Structural Results About On-line Learning Models With and Without Queries.
Machine Learning 36(3): 147-181 (1999) |
| 40 | | Philip M. Long:
The Complexity of Learning According to Two Models of a Drifting Environment.
Machine Learning 37(3): 337-354 (1999) |
| 39 | EE | Philip M. Long,
Apostol Natsev,
Jeffrey Scott Vitter:
Text compression via alphabet re-representation.
Neural Networks 12(4-5): 755-765 (1999) |
| 1998 |
|---|
| 38 | EE | Philip M. Long:
The complexity of learning according to two models of a drifting environment.
COLT 1998: 116-125 |
| 37 | EE | Philip M. Long:
On the Sample Complexity of Learning Functions with Bounded Variation.
COLT 1998: 126-133 |
| 36 | | Peter L. Bartlett,
Philip M. Long:
Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions.
J. Comput. Syst. Sci. 56(2): 174-190 (1998) |
| 35 | | Peter Auer,
Philip M. Long,
Aravind Srinivasan:
Approximating Hyper-Rectangles: Learning and Pseudorandom Sets.
J. Comput. Syst. Sci. 57(3): 376-388 (1998) |
| 34 | | Philip M. Long,
Lei Tan:
PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples.
Machine Learning 30(1): 7-21 (1998) |
| 1997 |
|---|
| 33 | EE | Philip M. Long:
On-line Evaluation and Prediction using Linear Functions.
COLT 1997: 21-31 |
| 32 | | Philip M. Long,
Apostol Natsev,
Jeffrey Scott Vitter:
Text Compression Via Alphabet Re-Representation.
Data Compression Conference 1997: 161-170 |
| 31 | EE | Peter Auer,
Philip M. Long,
Aravind Srinivasan:
Approximating Hyper-Rectangles: Learning and Pseudo-Random Sets.
STOC 1997: 314-323 |
| 30 | | Rakesh D. Barve,
Philip M. Long:
On the Complexity of Learning from Drifting Distributions.
Inf. Comput. 138(2): 170-193 (1997) |
| 29 | | Philip M. Long:
Guest Editor's Introduction.
Machine Learning 27(1): 5 (1997) |
| 1996 |
|---|
| 28 | | Philip M. Long:
Improved Bounds about On-line Learning of Smooth Functions of a Single Variable.
ALT 1996: 26-36 |
| 27 | EE | Rakesh D. Barve,
Philip M. Long:
On the Complexity of Learning from Drifting Distributions.
COLT 1996: 122-130 |
| 26 | EE | Philip M. Long,
Lei Tan:
PAC Learning Axis-Aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples.
COLT 1996: 228-234 |
| 25 | | Dzung T. Hoang,
Philip M. Long,
Jeffrey Scott Vitter:
Efficient Cost Measures for Motion Compensation at Low Bit Rates (Extended Abstract).
Data Compression Conference 1996: 102-111 |
| 24 | | Peter L. Bartlett,
Philip M. Long,
Robert C. Williamson:
Fat-Shattering and the Learnability of Real-Valued Functions.
J. Comput. Syst. Sci. 52(3): 434-452 (1996) |
| 1995 |
|---|
| 23 | EE | Peter L. Bartlett,
Philip M. Long:
More Theorems about Scale-sensitive Dimensions and Learning.
COLT 1995: 392-401 |
| 22 | | Dzung T. Hoang,
Philip M. Long,
Jeffrey Scott Vitter:
Multiple-Dictionary Coding Using Partial Matching.
Data Compression Conference 1995: 272-281 |
| 21 | | P. Krishnan,
Philip M. Long,
Jeffrey Scott Vitter:
Learning to Make Rent-to-Buy Decisions with Systems Applications.
ICML 1995: 233-330 |
| 20 | | Nick Littlestone,
Philip M. Long,
Manfred K. Warmuth:
On-line Learning of Linear Functions.
Computational Complexity 5(1): 1-23 (1995) |
| 19 | | David Haussler,
Philip M. Long:
A Generalization of Sauer's Lemma.
J. Comb. Theory, Ser. A 71(2): 219-240 (1995) |
| 18 | | Shai Ben-David,
Nicolò Cesa-Bianchi,
David Haussler,
Philip M. Long:
Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions.
J. Comput. Syst. Sci. 50(1): 74-86 (1995) |
| 17 | | Peter Auer,
Philip M. Long,
Wolfgang Maass,
Gerhard J. Woeginger:
On the Complexity of Function Learning.
Machine Learning 18(2-3): 187-230 (1995) |
| 16 | EE | Don Kimber,
Philip M. Long:
On-Line Learning of Smooth Functions of a Single Variable.
Theor. Comput. Sci. 148(1): 141-156 (1995) |
| 1994 |
|---|
| 15 | EE | Peter L. Bartlett,
Philip M. Long,
Robert C. Williamson:
Fat-Shattering and the Learnability of Real-Valued Functions.
COLT 1994: 299-310 |
| 14 | | Dzung T. Hoang,
Philip M. Long,
Jeffrey Scott Vitter:
Explicit Bit Minimization for Motion-Compensated Video Coding.
Data Compression Conference 1994: 175-184 |
| 13 | EE | Peter Auer,
Philip M. Long:
Simulating access to hidden information while learning.
STOC 1994: 263-272 |
| 12 | | Philip M. Long,
Manfred K. Warmuth:
Composite Geometric Concepts and Polynomial Predictability
Inf. Comput. 113(2): 230-252 (1994) |
| 11 | | Philip M. Long:
Halfspace Learning, Linear Programming, and Nonmalicious Distributions.
Inf. Process. Lett. 51(5): 245-250 (1994) |
| 10 | | David P. Helmbold,
Philip M. Long:
Tracking Drifting Concepts By Minimizing Disagreements.
Machine Learning 14(1): 27-45 (1994) |
| 1993 |
|---|
| 9 | EE | Peter Auer,
Philip M. Long,
Wolfgang Maass,
Gerhard J. Woeginger:
On the Complexity of Function Learning.
COLT 1993: 392-401 |
| 8 | EE | Nick Littlestone,
Philip M. Long:
On-Line Learning with Linear Loss Constraints.
COLT 1993: 412-421 |
| 7 | EE | Nicolò Cesa-Bianchi,
Philip M. Long,
Manfred K. Warmuth:
Worst-Case Quadratic Loss Bounds for a Generalization of the Widrow-Hoff Rule.
COLT 1993: 429-438 |
| 1992 |
|---|
| 6 | EE | Don Kimber,
Philip M. Long:
The Learning Complexity of Smooth Functions of a Single Variable.
COLT 1992: 153-159 |
| 5 | EE | Shai Ben-David,
Nicolò Cesa-Bianchi,
Philip M. Long:
Characterizations of Learnability for Classes of {O, ..., n}-Valued Functions.
COLT 1992: 333-340 |
| 4 | | David P. Helmbold,
Nick Littlestone,
Philip M. Long:
Apple Tasting and Nearly One-Sided Learning
FOCS 1992: 493-502 |
| 1991 |
|---|
| 3 | EE | David P. Helmbold,
Philip M. Long:
Tracking Drifting Concepts Using Random Examples.
COLT 1991: 13-23 |
| 2 | | Nick Littlestone,
Philip M. Long,
Manfred K. Warmuth:
On-Line Learning of Linear Functions
STOC 1991: 465-475 |
| 1990 |
|---|
| 1 | EE | Philip M. Long,
Manfred K. Warmuth:
Composite Geometric Concepts and Polynomial Predictability.
COLT 1990: 273-287 |
