Peter L. Bartlett Vis

*	2009
80	EE	Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin: A Stochastic View of Optimal Regret through Minimax Duality CoRR abs/0903.5328: (2009)
79	EE	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
78	EE	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
77	EE	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
76	EE	Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari: Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT 2008: 415-424
75	EE	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)
	2007
74	EE	Ambuj Tewari, Peter L. Bartlett: Bounded Parameter Markov Decision Processes with Average Reward Criterion. COLT 2007: 263-277
73	EE	Jacob Abernethy, Peter L. Bartlett, Alexander Rakhlin: Multitask Learning with Expert Advice. COLT 2007: 484-498
72	EE	Alexander Rakhlin, Jacob Abernethy, Peter L. Bartlett: Online discovery of similarity mappings. ICML 2007: 767-774
71	EE	Peter L. Bartlett, Elad Hazan, Alexander Rakhlin: Adaptive Online Gradient Descent. NIPS 2007
70	EE	Ambuj Tewari, Peter L. Bartlett: Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. NIPS 2007
	2006
69	EE	Peter L. Bartlett, Mikhail Traskin: AdaBoost is Consistent. NIPS 2006: 105-112
68	EE	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
67	EE	Peter L. Bartlett, Ambuj Tewari: Sample Complexity of Policy Search with Known Dynamics. NIPS 2006: 97-104
	2005
66	EE	Ambuj Tewari, Peter L. Bartlett: On the Consistency of Multiclass Classification Methods. COLT 2005: 143-157
	2004
65	EE	Peter L. Bartlett, Shahar Mendelson, Petra Philips: Local Complexities for Empirical Risk Minimization. COLT 2004: 270-284
64	EE	Peter L. Bartlett, Ambuj Tewari: Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. COLT 2004: 564-578
63	EE	Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester: Exponentiated Gradient Algorithms for Large-margin Structured Classification. NIPS 2004
62	EE	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)
61	EE	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
60	EE	Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe: Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. NIPS 2003
	2002
59	EE	Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson: Localized Rademacher Complexities. COLT 2002: 44-58
58		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
57	EE	Peter L. Bartlett: An Introduction to Reinforcement Learning Theory: Value Function Methods. Machine Learning Summer School 2002: 184-202
56		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)
55	EE	Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen: Exploiting Random Walks for Learning. Inf. Comput. 176(2): 121-135 (2002)
54	EE	Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. 64(1): 133-150 (2002)
53	EE	Llew Mason, Peter L. Bartlett, Mostefa Golea: Generalization Error of Combined Classifiers. J. Comput. Syst. Sci. 65(2): 415-438 (2002)
52	EE	Peter L. Bartlett, Shahar Mendelson: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. Journal of Machine Learning Research 3: 463-482 (2002)
51		Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi: Model Selection and Error Estimation. Machine Learning 48(1-3): 85-113 (2002)
50	EE	Peter L. Bartlett, Shai Ben-David: Hardness results for neural network approximation problems. Theor. Comput. Sci. 284(1): 53-66 (2002)
	2001
49	EE	Peter L. Bartlett, Shahar Mendelson: Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. COLT/EuroCOLT 2001: 224-240
48	EE	Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS 2001: 1507-1514
47	EE	Jonathan Baxter, Peter L. Bartlett: Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 319-350 (2001)
46	EE	Jonathan Baxter, Peter L. Bartlett, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 351-381 (2001)
	2000
45		Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141
44		Peter L. Bartlett, Stéphane Boucheron, Gábor Lugosi: Model Selection and Error Estimation. COLT 2000: 286-297
43		Jonathan Baxter, Peter L. Bartlett: Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML 2000: 41-48
42		Alex J. Smola, Peter L. Bartlett: Sparse Greedy Gaussian Process Regression. NIPS 2000: 619-625
41		Martin Anthony, Peter L. Bartlett: Function Learning From Interpolation. Combinatorics, Probability & Computing 9(3): (2000)
40		Llew Mason, Peter L. Bartlett, Jonathan Baxter: Improved Generalization Through Explicit Optimization of Margins. Machine Learning 38(3): 243-255 (2000)
39		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)
38		Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett: New Support Vector Algorithms. Neural Computation 12(5): 1207-1245 (2000)
	1999
37	EE	Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson: Covering Numbers for Support Vector Machines. COLT 1999: 267-277
36	EE	Peter L. Bartlett, Shai Ben-David: Hardness Results for Neural Network Approximation Problems. EuroCOLT 1999: 50-62
35	EE	Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean: Boosting Algorithms as Gradient Descent. NIPS 1999: 512-518
	1998
34	EE	Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS 1998: 190-196
33	EE	Llew Mason, Peter L. Bartlett, Jonathan Baxter: Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS 1998: 288-294
32	EE	Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson: Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336
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	EE	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	EE	Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni: Learning Changing Concepts by Exploiting the Structure of Change. COLT 1996: 131-139
18	EE	Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: The Importance of Convexity in Learning with Squared Loss. COLT 1996: 140-146
17	EE	John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony: A Framework for Structural Risk Minimisation. COLT 1996: 68-76
16	EE	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	EE	Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson: On Efficient Agnostic Learning of Linear Combinations of Basis Functions. COLT 1995: 369-376
11	EE	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	EE	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	EE	Peter L. Bartlett, Philip M. Long, Robert C. Williamson: Fat-Shattering and the Learnability of Real-Valued Functions. COLT 1994: 299-310
7	EE	Peter L. Bartlett, Paul Fischer, Klaus-Uwe Höffgen: Exploiting Random Walks for Learning. COLT 1994: 318-327
6	EE	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	EE	Peter L. Bartlett: Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks. COLT 1993: 144-150
4	EE	Peter L. Bartlett: Vapnik-Chervonenkis Dimension Bounds for Two- and Three-Layer Networks. Neural Computation 5(3): 371-373 (1993)
	1992
3	EE	Peter L. Bartlett: Learning With a Slowly Changing Distribution. COLT 1992: 243-252
	1991
2	EE	Peter L. Bartlett, Robert C. Williamson: Investigating the Distribution Assumptions in the Pac Learning Model. COLT 1991: 24-32
1	EE	Robert C. Williamson, Peter L. Bartlett: Splines, Rational Functions and Neural Networks. NIPS 1991: 1040-1047

Coauthor Index

1	Jacob Abernethy	[72] [73] [76] [80]
2	Alekh Agarwal	[80]
3	Martin Anthony	[10] [15] [17] [29] [41]
4	Marco Barreno	[78]
5	Jonathan Baxter	[22] [24] [33] [35] [40] [43] [45] [46] [47] [48] [54] [62]
6	Shai Ben-David	[19] [36] [39] [50]
7	Stéphane Boucheron	[44] [51]
8	Olivier Bousquet	[59]
9	Fuching Jack Chi	[78]
10	Michael Collins	[63]
11	Nello Cristianini	[58] [61]
12	Varsha Dani	[77]
13	Paul Fischer	[7] [55]
14	Marcus R. Frean	[35]
15	Laurent El Ghaoui	[58] [61]
16	Mostefa Golea	[23] [53]
17	Evan Greensmith	[48] [62]
18	Ying Guo	[37] [56]
19	Thomas P. Hayes	[77]
20	Elad Hazan	[71]
21	Klaus-Uwe Höffgen	[7] [55]
22	Yuval Ishai	[15]
23	Michael I. Jordan	[58] [60] [61]
24	Anthony D. Joseph	[78]
25	Sham M. Kakade (Sham Kakade)	[77]
26	Adam Kowalczyk	[9]
27	Sanjeev R. Kulkarni	[19] [21] [39]
28	Gert R. G. Lanckriet	[58] [61]
29	Wee Sun Lee	[6] [12] [14] [18] [20] [23] [28] [75]
30	Tamás Linder	[25] [30]
31	Philip M. Long	[8] [11] [13] [27]
32	Gábor Lugosi	[25] [30] [44] [51]
33	Vitaly Maiorov	[26] [34]
34	Llew Mason	[23] [33] [35] [40] [53]
35	David A. McAllester	[63]
36	Jon D. McAuliffe	[60]
37	Ron Meir (Ronny Meir)	[26] [34]
38	Shahar Mendelson	[49] [52] [59] [65]
39	Blaine Nelson	[78]
40	Petra Philips	[65]
41	S. E. Posner	[21]
42	Alexander Rakhlin	[71] [72] [73] [76] [77] [80]
43	Benjamin I. P. Rubinstein	[68] [78] [79]
44	J. Hyam Rubinstein	[68] [79]
45	Udam Saini	[78]
46	Bernhard Schölkopf	[32] [38]
47	John Shawe-Taylor	[15] [17] [29] [37] [56]
48	Alexander J. Smola (Alex J. Smola)	[32] [38] [42]
49	Jacek Szymanski	[9]
50	Benjamin Taskar (Ben Taskar)	[63]
51	Ambuj Tewari	[64] [66] [67] [70] [74] [76] [77]
52	Mikhail Traskin	[69]
53	J. Doug Tygar (J. D. Tygar)	[78]
54	Lex Weaver	[46]
55	Robert C. Williamson	[1] [2] [6] [8] [9] [12] [13] [14] [17] [18] [20] [28] [29] [32] [37] [38] [56] [75]

Colors in the list of coauthors