Philip M. Long

	2009
88		Adam R. Klivans, Philip M. Long, Alex K. Tang: Baum's Algorithm Learns Intersections of Halfspaces with Respect to Log-Concave Distributions. APPROX-RANDOM 2009: 588-600
87		Adam R. Klivans, Philip M. Long, Rocco A. Servedio: Learning Halfspaces with Malicious Noise. ICALP (1) 2009: 609-621
	2008
86		Philip M. Long, Rocco A. Servedio: Random classification noise defeats all convex potential boosters. ICML 2008: 608-615
85		Philip M. Long, Rocco A. Servedio: Adaptive Martingale Boosting. NIPS 2008: 977-984
84		Peter Auer, Philip M. Long: Guest editors' introduction: Special issue on learning theory. J. Comput. Syst. Sci. 74(8): 1227 (2008)
83		Philip M. Long, Frank Stephan: Preface. Theor. Comput. Sci. 405(3): 207-208 (2008)
	2007
82		Philip M. Long, Rocco A. Servedio: Boosting the Area under the ROC Curve. NIPS 2007
81		Zafer Barutçuoglu, Philip M. Long, Rocco A. Servedio: One-Pass Boosting. NIPS 2007
80		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		José L. Balcázar, Philip M. Long, Frank Stephan: Editors' Introduction. ALT 2006: 1-9
76		Philip M. Long, Rocco A. Servedio: Discriminative Learning Can Succeed Where Generative Learning Fails. COLT 2006: 319-334
75		Ofer Dekel, Philip M. Long, Yoram Singer: Online Multitask Learning. COLT 2006: 453-467
74		Yi Li, Philip M. Long: Learnability and the doubling dimension. NIPS 2006: 889-896
73		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		Philip M. Long, Rocco A. Servedio: Martingale Boosting. COLT 2005: 79-94
71		Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio: Unsupervised evidence integration. ICML 2005: 521-528
70		Sanjoy Dasgupta, Philip M. Long: Performance guarantees for hierarchical clustering. J. Comput. Syst. Sci. 70(4): 555-569 (2005)
	2004
69		Philip M. Long, Xinyu Wu: Mistake Bounds for Maximum Entropy Discrimination. NIPS 2004
68		Philip M. Long: Efficient algorithms for learning functions with bounded variation. Inf. Comput. 188(1): 99-115 (2004)
	2003
67		Sanjoy Dasgupta, Philip M. Long: Boosting with Diverse Base Classifiers. COLT 2003: 273-287
66		Naoki Abe, Alan W. Biermann, Philip M. Long: Reinforcement Learning with Immediate Rewards and Linear Hypotheses. Algorithmica 37(4): 263-293 (2003)
65		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		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		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		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		Philip M. Long: On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses. COLT/EuroCOLT 2001: 289-302
58		Shai Ben-David, Philip M. Long, Yishay Mansour: Agnostic Boosting. COLT/EuroCOLT 2001: 507-516
57		Wee Sun Lee, Philip M. Long: A Theoretical Analysis of Query Selection for Collaborative Filtering. COLT/EuroCOLT 2001: 517-528
56		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		Yi Li, Philip M. Long, Aravind Srinivasan: Improved bounds on the sample complexity of learning. SODA 2000: 309-318
51		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		Peter Auer, Philip M. Long: Simulating Access to Hidden Information while Learning Electronic Colloquium on Computational Complexity (ECCC) 7(67): (2000)
49		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		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		Yi Li, Philip M. Long: The Relaxed Online Maximum Margin Algorithm. NIPS 1999: 498-504
43		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		Philip M. Long, Apostol Natsev, Jeffrey Scott Vitter: Text compression via alphabet re-representation. Neural Networks 12(4-5): 755-765 (1999)
	1998
38		Philip M. Long: The complexity of learning according to two models of a drifting environment. COLT 1998: 116-125
37		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		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		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		Rakesh D. Barve, Philip M. Long: On the Complexity of Learning from Drifting Distributions. COLT 1996: 122-130
26		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		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		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		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		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		Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger: On the Complexity of Function Learning. COLT 1993: 392-401
8		Nick Littlestone, Philip M. Long: On-Line Learning with Linear Loss Constraints. COLT 1993: 412-421
7		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		Don Kimber, Philip M. Long: The Learning Complexity of Smooth Functions of a Single Variable. COLT 1992: 153-159
5		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		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		Philip M. Long, Manfred K. Warmuth: Composite Geometric Concepts and Polynomial Predictability. COLT 1990: 273-287

Coauthor Index

1	Naoki Abe	[45] [66]
2	Roger Anderson	[78]
3	Marta Arias	[78]
4	Peter Auer	[9] [13] [17] [31] [35] [41] [49] [50] [51] [84]
5	José L. Balcázar	[77] [79]
6	Peter L. Bartlett	[15] [23] [24] [36]
7	Zafer Barutçuoglu	[81]
8	Rakesh D. Barve	[27] [30]
9	Shai Ben-David	[5] [18] [53] [58] [64]
10	Alan W. Biermann	[66]
11	Albert Boulanger	[78]
12	Nicolò Cesa-Bianchi	[5] [7] [18]
13	Sanjoy Dasgupta	[63] [67] [70]
14	Ofer Dekel	[75]
15	Frank Doherty	[78]
16	Nadav Eiron	[53] [64]
17	William Fairechio	[78]
18	Sarah Gilman	[71]
19	Philip Gross	[78]
20	David Haussler	[18] [19]
21	David P. Helmbold	[3] [4] [10] [47] [48]
22	Dzung T. Hoang	[14] [22] [25] [42]
23	John A. Johnson	[78]
24	Don Kimber	[6] [16]
25	Adam R. Klivans (Adam Klivans)	[87] [88]
26	Matthew Koenig	[78]
27	Arthur Kressner	[78]
28	P. Krishnan	[21] [43]
29	Charles Lawson	[78]
30	Serena Lee	[78]
31	Wee Sun Lee	[57] [63]
32	Yi Li	[44] [52] [54] [55] [60] [74]
33	Nick Littlestone	[2] [4] [8] [20] [47] [48]
34	Wolfgang Maass	[9] [17] [51]
35	Yishay Mansour	[58]
36	Mark Mastrocinque	[78]
37	Apostol Natsev	[32] [39]
38	Vinsensius Berlian Vega SN	[62]
39	Rocco A. Servedio	[71] [72] [73] [76] [80] [81] [82] [85] [86] [87]
40	Hans-Ulrich Simon	[80]
41	Yoram Singer	[75]
42	Aravind Srinivasan	[31] [35] [49] [52] [54] [55]
43	Frank Stephan	[77] [79] [83]
44	Lei Tan	[26] [34]
45	Alex K. Tang	[88]
46	Mark Treshock	[71]
47	Vinay Varadan	[71]
48	Jeffrey Scott Vitter	[14] [21] [22] [25] [32] [39] [42] [43]
49	David L. Waltz	[78]
50	Manfred K. Warmuth	[1] [2] [7] [12] [20]
51	Robert C. Williamson	[15] [24]
52	Gerhard J. Woeginger	[9] [17] [51]
53	Xinyu Wu	[69]

Colors in the list of coauthors