John Langford

2009
61	EE	Daniel Hsu, Sham M. Kakade, John Langford, Tong Zhang: Multi-Label Prediction via Compressed Sensing CoRR abs/0902.1284: (2009)
60	EE	Kilian Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex J. Smola: Feature Hashing for Large Scale Multitask Learning CoRR abs/0902.2206: (2009)
59	EE	Alina Beygelzimer, John Langford, Pradeep Ravikumar: Error-Correcting Tournaments CoRR abs/0902.3176: (2009)
58	EE	Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory B. Sorkin, Alexander L. Strehl: Conditional Probability Tree Estimation Analysis and Algorithms CoRR abs/0903.4217: (2009)
57	EE	Nicholas J. Hopper, Luis von Ahn, John Langford: Provably Secure Steganography. IEEE Trans. Computers 58(5): 662-676 (2009)
56	EE	Maria-Florina Balcan, Alina Beygelzimer, John Langford: Agnostic active learning. J. Comput. Syst. Sci. 75(1): 78-89 (2009)
2008
55	EE	Nicolas S. Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel M. Reeves, Yoav Shoham, David M. Pennock: Self-financed wagering mechanisms for forecasting. ACM Conference on Electronic Commerce 2008: 170-179
54	EE	John Langford, Alexander L. Strehl, Jennifer Wortman: Exploration scavenging. ICML 2008: 528-535
53	EE	Sharad Goel, John Langford, Alexander L. Strehl: Predictive Indexing for Fast Search. NIPS 2008: 505-512
52	EE	John Langford, Lihong Li, Tong Zhang: Sparse Online Learning via Truncated Gradient. NIPS 2008: 905-912
51	EE	John Langford, Lihong Li, Tong Zhang: Sparse Online Learning via Truncated Gradient CoRR abs/0806.4686: (2008)
50	EE	Alina Beygelzimer, John Langford: The Offset Tree for Learning with Partial Labels CoRR abs/0812.4044: (2008)
49	EE	Alina Beygelzimer, Sanjoy Dasgupta, John Langford: Importance Weighted Active Learning CoRR abs/0812.4952: (2008)
48	EE	Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin: Robust reductions from ranking to classification. Machine Learning 72(1-2): 139-153 (2008)
2007
47	EE	Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin: Robust Reductions from Ranking to Classification. COLT 2007: 604-619
46	EE	John Langford, Tong Zhang: The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. NIPS 2007
45	EE	Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, John Langford: Maintaining Equilibria During Exploration in Sponsored Search Auctions. WINE 2007: 119-130
44	EE	Peter Grünwald, John Langford: Suboptimal behavior of Bayes and MDL in classification under misspecification. Machine Learning 66(2-3): 119-149 (2007)
2006
43	EE	Jacob Abernethy, John Langford, Manfred K. Warmuth: Continuous Experts and the Binning Algorithm. COLT 2006: 544-558
42	EE	Maria-Florina Balcan, Alina Beygelzimer, John Langford: Agnostic active learning. ICML 2006: 65-72
41	EE	Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman: PAC model-free reinforcement learning. ICML 2006: 881-888
40	EE	Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor. ICML 2006: 97-104
39	EE	Naoki Abe, Bianca Zadrozny, John Langford: Outlier detection by active learning. KDD 2006: 504-509
38	EE	John Langford, Roberto Oliveira, Bianca Zadrozny: Predicting Conditional Quantiles via Reduction to Classification. UAI 2006
37	EE	John Langford, Jeffrey Roy: E-government and public-private partnerships in Canada: when failure is no longer an option. IJEB 4(2): 118-135 (2006)
2005
36		Alina Beygelzimer, John Langford, Bianca Zadrozny: Weighted One-Against-All. AAAI 2005: 720-725
35	EE	John Langford, Alina Beygelzimer: Sensitive Error Correcting Output Codes. COLT 2005: 158-172
34	EE	John Langford: The Cross Validation Problem. COLT 2005: 687-688
33	EE	Matti Kääriäinen, John Langford: A comparison of tight generalization error bounds. ICML 2005: 409-416
32	EE	John Langford, Bianca Zadrozny: Relating reinforcement learning performance to classification performance. ICML 2005: 473-480
31	EE	Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny: Error limiting reductions between classification tasks. ICML 2005: 49-56
30	EE	Luis von Ahn, Nicholas J. Hopper, John Langford: Covert two-party computation. STOC 2005: 513-522
29	EE	John Langford: Tutorial on Practical Prediction Theory for Classification. Journal of Machine Learning Research 6: 273-306 (2005)
2004
28	EE	Peter Grünwald, John Langford: Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification. COLT 2004: 331-347
27	EE	Naoki Abe, Bianca Zadrozny, John Langford: An iterative method for multi-class cost-sensitive learning. KDD 2004: 3-11
26	EE	Arindam Banerjee, John Langford: An objective evaluation criterion for clustering. KDD 2004: 515-520
25	EE	Peter Grünwald, John Langford: Suboptimal behaviour of Bayes and MDL in classification under misspecification CoRR math.ST/0406221: (2004)
24	EE	Luis von Ahn, Manuel Blum, John Langford: Telling humans and computers apart automatically. Commun. ACM 47(2): 56-60 (2004)
23	EE	Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford: Reductions Between Classification Tasks Electronic Colloquium on Computational Complexity (ECCC)(077): (2004)
22	EE	John Langford, David A. McAllester: Computable Shell Decomposition Bounds. Journal of Machine Learning Research 5: 529-547 (2004)
2003
21	EE	Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz: Correlated equilibria in graphical games. ACM Conference on Electronic Commerce 2003: 42-47
20	EE	Avrim Blum, John Langford: PAC-MDL Bounds. COLT 2003: 344-357
19	EE	Luis von Ahn, Manuel Blum, Nicholas J. Hopper, John Langford: CAPTCHA: Using Hard AI Problems for Security. EUROCRYPT 2003: 294-311
18	EE	Bianca Zadrozny, John Langford, Naoki Abe: Cost-Sensitive Learning by Cost-Proportionate Example Weighting. ICDM 2003: 435-
17		Sham Kakade, Michael J. Kearns, John Langford: Exploration in Metric State Spaces. ICML 2003: 306-312
16	EE	John Langford, Avrim Blum: Microchoice Bounds and Self Bounding Learning Algorithms. Machine Learning 51(2): 165-179 (2003)
2002
15	EE	Nicholas J. Hopper, John Langford, Luis von Ahn: Provably Secure Steganography. CRYPTO 2002: 77-92
14		Sham Kakade, John Langford: Approximately Optimal Approximate Reinforcement Learning. ICML 2002: 267-274
13		John Langford: Combining Trainig Set and Test Set Bounds. ICML 2002: 331-338
12		John Langford, Martin Zinkevich, Sham Kakade: Competitive Analysis of the Explore/Exploit Tradeoff. ICML 2002: 339-346
11	EE	John Langford, John Shawe-Taylor: PAC-Bayes & Margins. NIPS 2002: 423-430
2001
10		John Langford, Matthias Seeger, Nimrod Megiddo: An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML 2001: 290-297
9	EE	John Langford, Rich Caruana: (Not) Bounding the True Error. NIPS 2001: 809-816
8	EE	Sebastian Thrun, John Langford, Vandi Verma: Risk Sensitive Particle Filters. NIPS 2001: 961-968
2000
7		John Langford, David A. McAllester: Computable Shell Decomposition Bounds. COLT 2000: 25-34
6		Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum: FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML 2000: 703-710
1999
5	EE	Avrim Blum, Adam Kalai, John Langford: Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT 1999: 203-208
4	EE	John Langford, Avrim Blum: Microchoice Bounds and Self Bounding Learning Algorithms. COLT 1999: 209-214
3		Avrim Blum, John Langford: Probabilistic Planning in the Graphplan Framework. ECP 1999: 319-332
2		Sebastian Thrun, John Langford, Dieter Fox: Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes. ICML 1999: 415-424
1998
1	EE	Avrim Blum, Carl Burch, John Langford: On Learning Monotone Boolean Functions. FOCS 1998: 408-415

Coauthor Index

1	Naoki Abe	[18] [27] [39]
2	Jacob Abernethy	[43]
3	Luis von Ahn	[15] [19] [24] [30] [57]
4	Josh Attenberg	[60]
5	Maria-Florina Balcan (Maria-Florina Popa)	[42] [47] [48] [56]
6	Arindam Banerjee	[26]
7	Nikhil Bansal	[47] [48]
8	Alina Beygelzimer	[23] [31] [35] [36] [40] [42] [47] [48] [49] [50] [56] [58] [59]
9	Avrim Blum	[1] [3] [4] [5] [6] [16] [20]
10	Manuel Blum	[19] [24]
11	Carl Burch	[1]
12	Rich Caruana	[6] [9]
13	Yiling Chen	[55]
14	Don Coppersmith	[47] [48]
15	Varsha Dani	[23] [31]
16	Anirban Dasgupta	[60]
17	Sanjoy Dasgupta	[49]
18	Dieter Fox	[2]
19	Sharad Goel	[53]
20	Peter Grünwald (Peter D. Grünwald, Peter Grunwald)	[25] [28] [44]
21	Thomas P. Hayes	[23] [31]
22	Nicholas J. Hopper	[15] [19] [30] [57]
23	Daniel Hsu	[61]
24	Matti Kääriäinen	[33]
25	Sham M. Kakade (Sham Kakade)	[12] [14] [17] [21] [40] [61]
26	Adam Tauman Kalai (Adam Kalai)	[5]
27	Michael J. Kearns	[17] [21]
28	Nicolas S. Lambert	[55]
29	Lihong Li	[41] [45] [51] [52]
30	Yuri Lifshits	[58]
31	Michael L. Littman	[41]
32	David A. McAllester	[7] [22]
33	Nimrod Megiddo	[10]
34	Joseph O'Sullivan	[6]
35	Roberto Oliveira	[38]
36	Luis E. Ortiz	[21]
37	David M. Pennock (David Pennock)	[55]
38	Pradeep Ravikumar	[59]
39	Daniel M. Reeves	[55]
40	Jeffrey Roy	[37]
41	Matthias Seeger	[10]
42	John Shawe-Taylor	[11]
43	Yoav Shoham	[55]
44	Alexander J. Smola (Alex J. Smola)	[60]
45	Gregory B. Sorkin	[47] [48] [58]
46	Alexander L. Strehl	[41] [53] [54] [58]
47	Sebastian Thrun	[2] [8]
48	Vandi Verma	[8]
49	Yevgeniy Vorobeychik	[45]
50	Manfred K. Warmuth	[43]
51	Kilian Weinberger	[60]
52	Eric Wiewiora	[41]
53	Jennifer Wortman	[45] [54] [55]
54	Bianca Zadrozny	[18] [27] [31] [32] [36] [38] [39]
55	Tong Zhang	[46] [51] [52] [61]
56	Martin Zinkevich	[12]

Colors in the list of coauthors