Peter Bartlett
Peter Bartlett
Professor, EECS and Statistics, UC Berkeley
Verified email at - Homepage
Cited by
Cited by
Boosting the margin: A new explanation for the effectiveness of voting methods
RE Schapire, Y Freund, P Bartlett, WS Lee
The annals of statistics 26 (5), 1651-1686, 1998
New support vector algorithms
B Schölkopf, AJ Smola, RC Williamson, PL Bartlett
Neural computation 12 (5), 1207-1245, 2000
Learning the kernel matrix with semidefinite programming
GRG Lanckriet, N Cristianini, P Bartlett, LE Ghaoui, MI Jordan
Journal of Machine learning research 5 (Jan), 27-72, 2004
Rademacher and Gaussian complexities: Risk bounds and structural results
PL Bartlett, S Mendelson
Journal of Machine Learning Research 3 (Nov), 463-482, 2002
Neural network learning: Theoretical foundations
M Anthony, PL Bartlett
cambridge university press, 2009
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
PL Bartlett
IEEE transactions on Information Theory 44 (2), 525-536, 1998
Regularization networks and support vector machines
T Evgeniou, M Pontil, T Poggio
Advances in computational mathematics 13 (1), 1, 2000
Convexity, classification, and risk bounds
PL Bartlett, MI Jordan, JD McAuliffe
Journal of the American Statistical Association 101 (473), 138-156, 2006
Boosting algorithms as gradient descent
L Mason, J Baxter, PL Bartlett, MR Frean
Advances in neural information processing systems, 512-518, 2000
FaST linear mixed models for genome-wide association studies
C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, D Heckerman
Nature methods 8 (10), 833-835, 2011
Infinite-horizon policy-gradient estimation
J Baxter, PL Bartlett
Journal of Artificial Intelligence Research 15, 319-350, 2001
Structural risk minimization over data-dependent hierarchies
J Shawe-Taylor, PL Bartlett, RC Williamson, M Anthony
IEEE transactions on Information Theory 44 (5), 1926-1940, 1998
Local rademacher complexities
PL Bartlett, O Bousquet, S Mendelson
The Annals of Statistics 33 (4), 1497-1537, 2005
Spectrally-normalized margin bounds for neural networks
PL Bartlett, DJ Foster, MJ Telgarsky
Advances in neural information processing systems, 6240-6249, 2017
Learning the kernel function via regularization
CA Micchelli, M Pontil
Journal of machine learning research 6 (Jul), 1099-1125, 2005
Generalized support vector machines
O Mangasarian
RL: Fast Reinforcement Learning via Slow Reinforcement Learning
Y Duan, J Schulman, X Chen, PL Bartlett, I Sutskever, P Abbeel
arXiv preprint arXiv:1611.02779, 2016
Sparse greedy Gaussian process regression
AJ Smola, PL Bartlett
Advances in neural information processing systems, 619-625, 2001
Model selection and error estimation
PL Bartlett, S Boucheron, G Lugosi
Machine Learning 48 (1-3), 85-113, 2002
Generalization performance of support vector machines and other pattern classifiers
P Bartlett, J Shawe-Taylor
Advances in Kernel methods—support vector learning, 43-54, 1999
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