Peter L. Bartlett

	2012
117		Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett: A Learning-Based Approach to Reactive Security. IEEE Trans. Dependable Sec. Comput. 9(4): 482-493 (2012)
116		Alekh Agarwal, Peter L. Bartlett, Pradeep D. Ravikumar, Martin J. Wainwright: Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization. IEEE Transactions on Information Theory 58(5): 3235-3249 (2012)
115		Fares Hedayati, Peter L. Bartlett: Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior. Journal of Machine Learning Research - Proceedings Track 22: 504-510 (2012)
	2011
114		John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger: Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. NIPS 2011
113		Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett: Learning with Missing Features. UAI 2011: 635-642
112		Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett: Online and Batch Learning Algorithms for Data with Missing Features CoRR abs/1104.0729: (2011)
111		Peter L. Bartlett, Jonathan Baxter: Infinite-Horizon Policy-Gradient Estimation CoRR abs/1106.0665: (2011)
110		Peter L. Bartlett, Jonathan Baxter, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation CoRR abs/1106.0666: (2011)
109		Jacob Abernethy, Peter L. Bartlett, Elad Hazan: Blackwell Approachability and No-Regret Learning are Equivalent. Journal of Machine Learning Research - Proceedings Track 19: 27-46 (2011)
108		Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clement Levrard: Oracle inequalities for computationally budgeted model selection. Journal of Machine Learning Research - Proceedings Track 19: 69-86 (2011)
	2010
107		Jacob Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton: A Regularization Approach to Metrical Task Systems. ALT 2010: 270-284
106		Peter L. Bartlett: Optimal Online Prediction in Adversarial Environments. ALT 2010: 34
105		Peter L. Bartlett: Optimal Online Prediction in Adversarial Environments. Discovery Science 2010: 371
104		Marius Kloft, Ulrich Rückert, Peter L. Bartlett: A Unifying View of Multiple Kernel Learning. ECML/PKDD (2) 2010: 66-81
103		Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Song, Peter L. Bartlett: A Learning-Based Approach to Reactive Security. Financial Cryptography 2010: 192-206
102		Brian Kulis, Peter L. Bartlett: Implicit Online Learning. ICML 2010: 575-582
101		Marius Kloft, Ulrich Rückert, Peter L. Bartlett: A Unifying View of Multiple Kernel Learning CoRR abs/1005.0437: (2010)
100		Jacob Abernethy, Peter L. Bartlett, Elad Hazan: Blackwell Approachability and Low-Regret Learning are Equivalent CoRR abs/1011.1936: (2010)
99		Peter L. Bartlett: Learning to act in uncertain environments: technical perspective. Commun. ACM 53(5): 98 (2010)
98		Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein: Corrigendum to "Shifting: One-inclusion mistake bounds and sample compression" [J. Comput. System Sci 75 (1) (2009) 37-59]. J. Comput. Syst. Sci. 76(3-4): 278-280 (2010)
97		Alekh Agarwal, Peter L. Bartlett, Max Dama: Optimal Allocation Strategies for the Dark Pool Problem. Journal of Machine Learning Research - Proceedings Track 9: 9-16 (2010)
	2009
96		Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin: A Stochastic View of Optimal Regret through Minimax Duality. COLT 2009
95		Alekh Agarwal, Peter L. Bartlett, Pradeep D. Ravikumar, Martin J. Wainwright: Information-theoretic lower bounds on the oracle complexity of convex optimization. NIPS 2009: 1-9
94		Peter L. Bartlett, Ambuj Tewari: REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. UAI 2009: 35-42
93		Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin: A Stochastic View of Optimal Regret through Minimax Duality CoRR abs/0903.5328: (2009)
92		Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft: Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning CoRR abs/0911.5708: (2009)
91		Adam Barth, Benjamin I. P. Rubinstein, Mukund Sundararajan, John C. Mitchell, Dawn Xiaodong Song, Peter L. Bartlett: A Learning-Based Approach to Reactive Security CoRR abs/0912.1155: (2009)
90		Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein: Shifting: One-inclusion mistake bounds and sample compression. J. Comput. Syst. Sci. 75(1): 37-59 (2009)
	2008
89		Marco Barreno, Peter L. Bartlett, Fuching Jack Chi, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, Udam Saini, J. Doug Tygar: Open problems in the security of learning. AISec 2008: 19-26
88		Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham Kakade, Alexander Rakhlin, Ambuj Tewari: High-Probability Regret Bounds for Bandit Online Linear Optimization. COLT 2008: 335-342
87		Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari: Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT 2008: 415-424
86		Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Correction to "The Importance of Convexity in Learning With Squared Loss". IEEE Transactions on Information Theory 54(9): 4395 (2008)
85		Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett: Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. Journal of Machine Learning Research 9: 1775-1822 (2008)
84		Peter L. Bartlett, Marten H. Wegkamp: Classification with a Reject Option using a Hinge Loss. Journal of Machine Learning Research 9: 1823-1840 (2008)
	2007
83		Ambuj Tewari, Peter L. Bartlett: Bounded Parameter Markov Decision Processes with Average Reward Criterion. COLT 2007: 263-277
82		Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin: Multitask Learning with Expert Advice. COLT 2007: 484-498
81		Alexander Rakhlin, Jacob Abernethy, Peter L. Bartlett: Online discovery of similarity mappings. ICML 2007: 767-774
80		Peter L. Bartlett, Elad Hazan, Alexander Rakhlin: Adaptive Online Gradient Descent. NIPS 2007
79		Ambuj Tewari, Peter L. Bartlett: Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. NIPS 2007
78		Ambuj Tewari, Peter L. Bartlett: On the Consistency of Multiclass Classification Methods. Journal of Machine Learning Research 8: 1007-1025 (2007)
77		Peter L. Bartlett, Mikhail Traskin: AdaBoost is Consistent. Journal of Machine Learning Research 8: 2347-2368 (2007)
76		Peter L. Bartlett, Ambuj Tewari: Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. Journal of Machine Learning Research 8: 775-790 (2007)
75		David S. Rosenberg, Peter L. Bartlett: The Rademacher Complexity of Co-Regularized Kernel Classes. Journal of Machine Learning Research - Proceedings Track 2: 396-403 (2007)
	2006
74		Peter L. Bartlett, Mikhail Traskin: AdaBoost is Consistent. NIPS 2006: 105-112
73		Benjamin I. P. Rubinstein, Peter L. Bartlett, J. Hyam Rubinstein: Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds. NIPS 2006: 1193-1200
72		Peter L. Bartlett, Ambuj Tewari: Sample Complexity of Policy Search with Known Dynamics. NIPS 2006: 97-104
	2005
71		Ambuj Tewari, Peter L. Bartlett: On the Consistency of Multiclass Classification Methods. COLT 2005: 143-157
	2004
70		Peter L. Bartlett, Shahar Mendelson, Petra Philips: Local Complexities for Empirical Risk Minimization. COLT 2004: 270-284
69		Peter L. Bartlett, Ambuj Tewari: Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. COLT 2004: 564-578
68		Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester: Exponentiated Gradient Algorithms for Large-margin Structured Classification. NIPS 2004
67		Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. Journal of Machine Learning Research 5: 1471-1530 (2004)
66		Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research 5: 27-72 (2004)
	2003
65		Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe: Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003
	2002
64		Martin Anthony, Peter L. Bartlett: Neural Network Learning - Theoretical Foundations. Cambridge University Press 2002: I-XIV, 1-389
63		Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson: Localized Rademacher Complexities. COLT 2002: 44-58
62		Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan: Learning the Kernel Matrix with Semi-Definite Programming. ICML 2002: 323-330
61		Peter L. Bartlett: An Introduction to Reinforcement Learning Theory: Value Function Methods. Machine Learning Summer School 2002: 184-202
60		Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering numbers for support vector machines. IEEE Transactions on Information Theory 48(1): 239-250 (2002)
59		Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen: Exploiting Random Walks for Learning. Inf. Comput. 176(2): 121-135 (2002)
58		Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. 64(1): 133-150 (2002)
57		Llew Mason, Peter L. Bartlett, Mostefa Golea: Generalization Error of Combined Classifiers. J. Comput. Syst. Sci. 65(2): 415-438 (2002)
56		Peter L. Bartlett, Shahar Mendelson: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. Journal of Machine Learning Research 3: 463-482 (2002)
55		Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi: Model Selection and Error Estimation. Machine Learning 48(1-3): 85-113 (2002)
54		Peter L. Bartlett, Shai Ben-David: Hardness results for neural network approximation problems. Theor. Comput. Sci. 284(1): 53-66 (2002)
	2001
53		Peter L. Bartlett, Shahar Mendelson: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. COLT/EuroCOLT 2001: 224-240
52		Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS 2001: 1507-1514
51		Jonathan Baxter, Peter L. Bartlett: Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 319-350 (2001)
50		Jonathan Baxter, Peter L. Bartlett, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 351-381 (2001)
	2000
49		Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141
48		Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi: Model Selection and Error Estimation. COLT 2000: 286-297
47		Jonathan Baxter, Peter L. Bartlett: Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML 2000: 41-48
46		Alex J. Smola, Peter L. Bartlett: Sparse Greedy Gaussian Process Regression. NIPS 2000: 619-625
45		Leonardo C. Kammer, Robert R. Bitmead, Peter L. Bartlett: Direct iterative tuning via spectral analysis. Automatica 36(9): 1301-1307 (2000)
44		Martin Anthony, Peter L. Bartlett: Function Learning From Interpolation. Combinatorics, Probability & Computing 9(3): 213-225 (2000)
43		Sri Parameswaran, Matthew F. Parkinson, Peter L. Bartlett: Profiling in the ASP codesign environment. Journal of Systems Architecture 46(14): 1263-1274 (2000)
42		Llew Mason, Peter L. Bartlett, Jonathan Baxter: Improved Generalization Through Explicit Optimization of Margins. Machine Learning 38(3): 243-255 (2000)
41		Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni: Learning Changing Concepts by Exploiting the Structure of Change. Machine Learning 41(2): 153-174 (2000)
40		Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett: New Support Vector Algorithms. Neural Computation 12(5): 1207-1245 (2000)
	1999
39		Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering Numbers for Support Vector Machines. COLT 1999: 267-277
38		Peter L. Bartlett, Shai Ben-David: Hardness Results for Neural Network Approximation Problems. EuroCOLT 1999: 50-62
37		Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean: Boosting Algorithms as Gradient Descent. NIPS 1999: 512-518
	1998
36		Peter L. Bartlett, Yishay Mansour: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, July 24-26, 1998. ACM 1998
35		Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS 1998: 190-196
34		Llew Mason, Peter L. Bartlett, Jonathan Baxter: Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS 1998: 288-294
33		Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson: Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336
32		Leonardo C. Kammer, Robert R. Bitmead, Peter L. Bartlett: Optimal controller properties from closed-loop experiments. Automatica 34(1): 83-91 (1998)
31		Peter L. Bartlett: The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network. IEEE Transactions on Information Theory 44(2): 525-536 (1998)
30		Peter L. Bartlett, Tamás Linder, Gábor Lugosi: The Minimax Distortion Redundancy in Empirical Quantizer Design. IEEE Transactions on Information Theory 44(5): 1802-1813 (1998)
29		John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Transactions on Information Theory 44(5): 1926-1940 (1998)
28		Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. IEEE Transactions on Information Theory 44(5): 1974-1980 (1998)
27		Peter L. Bartlett, Philip M. Long: Prediction, Learning, Uniform Convergence, and Scale-Sensitive Dimensions. J. Comput. Syst. Sci. 56(2): 174-190 (1998)
26		Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. Neural Computation 10(8): 2159-2173 (1998)
	1997
25		Peter L. Bartlett, Tamás Linder, Gábor Lugosi: A Minimax Lower Bound for Empirical Quantizer Design. EuroCOLT 1997: 210-222
24		Jonathan Baxter, Peter L. Bartlett: A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks. EuroCOLT 1997: 251-259
23		Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason: Generalization in Decision Trees and DNF: Does Size Matter? NIPS 1997
22		Jonathan Baxter, Peter L. Bartlett: The Canonical Distortion Measure in Feature Space and 1-NN Classification. NIPS 1997
21		Peter L. Bartlett, Sanjeev R. Kulkarni, S. E. Posner: Covering numbers for real-valued function classes. IEEE Transactions on Information Theory 43(5): 1721-1724 (1997)
20		Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'. Neural Computation 9(4): 765-769 (1997)
	1996
19		Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni: Learning Changing Concepts by Exploiting the Structure of Change. COLT 1996: 131-139
18		Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146
17		John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: A Framework for Structural Risk Minimisation. COLT 1996: 68-76
16		Peter L. Bartlett: For Valid Generalization the Size of the Weights is More Important than the Size of the Network. NIPS 1996: 134-140
15		Martin Anthony, Peter L. Bartlett, Yuval Ishai, John Shawe-Taylor: Valid Generalisation from Approximate Interpolation. Combinatorics, Probability & Computing 5: 191-214 (1996)
14		Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Efficient agnostic learning of neural networks with bounded fan-in. IEEE Transactions on Information Theory 42(6): 2118-2132 (1996)
13		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
12		Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376
11		Peter L. Bartlett, Philip M. Long: More Theorems about Scale-sensitive Dimensions and Learning. COLT 1995: 392-401
10		Martin Anthony, Peter L. Bartlett: Function learning from interpolation. EuroCOLT 1995: 211-221
9		Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson: Examples of learning curves from a modified VC-formalism. NIPS 1995: 344-350
	1994
8		Peter L. Bartlett, Philip M. Long, Robert C. Williamson: Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310
7		Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen: Exploiting Random Walks for Learning. COLT 1994: 318-327
6		Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. COLT 1994: 362-367
	1993
5		Peter L. Bartlett: Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. COLT 1993: 144-150
4		Peter L. Bartlett: Vapnik-Chervonenkis Dimension Bounds for Two- and Three-Layer Networks. Neural Computation 5(3): 371-373 (1993)
	1992
3		Peter L. Bartlett: Learning With a Slowly Changing Distribution. COLT 1992: 243-252
	1991
2		Peter L. Bartlett, Robert C. Williamson: Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32
1		Robert C. Williamson, Peter L. Bartlett: Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047

Coauthor Index

1	Jacob Abernethy	[81] [82] [87] [93] [96] [100] [107] [109]
2	Alekh Agarwal	[93] [95] [96] [97] [108] [112] [113] [116]
3	Martin Anthony	[10] [15] [17] [29] [44] [64]
4	Marco Barreno	[89]
5	Adam Barth	[91] [103] [117]
6	Jonathan Baxter	[22] [24] [34] [37] [42] [47] [49] [50] [51] [52] [58] [67] [110] [111]
7	Shai Ben-David	[19] [38] [41] [54]
8	Robert R. Bitmead	[32] [45]
9	Stéphane Boucheron	[48] [55]
10	Olivier Bousquet	[63]
11	Niv Buchbinder	[107]
12	Xavier Carreras	[85]
13	Fuching Jack Chi	[89]
14	Michael Collins	[68] [85]
15	Nello Cristianini	[62] [66]
16	Max Dama	[97]
17	Varsha Dani	[88]
18	John C. Duchi	[108]
19	Paul Fischer	[7] [59]
20	Marcus R. Frean	[37]
21	Laurent El Ghaoui	[62] [66]
22	Amir Globerson	[85]
23	Mostefa Golea	[23] [57]
24	Evan Greensmith	[52] [67]
25	Ying Guo	[39] [60]
26	Thomas P. Hayes (Tom Hayes)	[88]
27	Elad Hazan	[80] [100] [109]
28	Fares Hedayati	[115]
29	Klaus-Uwe Höffgen	[7] [59]
30	Ling Huang	[92]
31	Yuval Ishai	[15]
32	Michael I. Jordan	[62] [65] [66]
33	Anthony D. Joseph	[89]
34	Sham M. Kakade (Sham Kakade)	[88]
35	Leonardo C. Kammer	[32] [45]
36	Marius Kloft	[101] [104]
37	Terry Koo	[85]
38	Adam Kowalczyk	[9]
39	Brian Kulis	[102]
40	Sanjeev R. Kulkarni	[19] [21] [41]
41	Gert R. G. Lanckriet	[62] [66]
42	Wee Sun Lee	[6] [12] [14] [18] [20] [23] [28] [86]
43	Clement Levrard	[108]
44	Tamás Linder	[25] [30]
45	Philip M. Long	[8] [11] [13] [27]
46	Gábor Lugosi	[25] [30] [48] [55]
47	Vitaly Maiorov (V. E. Maiorov)	[26] [35]
48	Yishay Mansour	[36]
49	Llew Mason	[23] [34] [37] [42] [57]
50	David A. McAllester	[68]
51	Jon D. McAuliffe	[65]
52	Ron Meir (Ronny Meir, R. S. Meir)	[26] [35]
53	Shahar Mendelson	[53] [56] [63] [70]
54	John C. Mitchell	[91] [103] [117]
55	Blaine Nelson	[89]
56	Sri Parameswaran	[43]
57	Matthew F. Parkinson	[43]
58	Fernando C. N. Pereira	[114]
59	Petra Philips	[70]
60	S. E. Posner	[21]
61	Alexander Rakhlin	[80] [81] [82] [87] [88] [93] [96]
62	Pradeep D. Ravikumar	[95] [116]
63	David S. Rosenberg	[75]
64	Afshin Rostamizadeh	[112] [113]
65	Benjamin I. P. Rubinstein	[73] [89] [90] [91] [92] [98] [103] [117]
66	J. Hyam Rubinstein	[73] [90] [98]
67	Ulrich Rückert	[101] [104]
68	Udam Saini	[89]
69	Bernhard Schölkopf	[33] [40]
70	John Shawe-Taylor	[15] [17] [29] [39] [60] [114]
71	Alexander J. Smola (Alex J. Smola)	[33] [40] [46]
72	Dawn Xiaodong Song (Dawn Song)	[91] [103] [117]
73	Isabelle Stanton	[107]
74	Mukund Sundararajan	[91] [103] [117]
75	Jacek Szymanski	[9]
76	Nina Taft (Nina Taft Plotkin)	[92]
77	Benjamin Taskar (Ben Taskar)	[68]
78	Ambuj Tewari	[69] [71] [72] [76] [78] [79] [83] [87] [88] [94]
79	Mikhail Traskin	[74] [77]
80	J. Doug Tygar (J. D. Tygar)	[89]
81	Martin J. Wainwright	[95] [116]
82	Lex Weaver	[50] [110]
83	Marten H. Wegkamp	[84]
84	Kilian Q. Weinberger	[114]
85	Robert C. Williamson	[1] [2] [6] [8] [9] [12] [13] [14] [17] [18] [20] [28] [29] [33] [39] [40] [60] [86]
86	Richard S. Zemel	[114]

Colors in the list of coauthors

Last update Sun May 27 04:04:01 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page