Peter Bartlett
Peter Bartlett
Professor, EECS and Statistics, UC Berkeley
Verified email at cs.berkeley.edu - Homepage
Title
Cited by
Cited by
Year
Boosting the margin: A new explanation for the effectiveness of voting methods
RE Schapire, Y Freund, P Bartlett, WS Lee
Annals of statistics 26 (5), 1651-1686, 1998
33701998
New support vector algorithms
B Schölkopf, AJ Smola, RC Williamson, PL Bartlett
Neural computation 12 (5), 1207-1245, 2000
33132000
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
28352004
Regularization networks and support vector machines
T Evgeniou, M Pontil, T Poggio
Advances in computational mathematics 13 (1), 1-50, 2000
1916*2000
Rademacher and Gaussian complexities: Risk bounds and structural results
PL Bartlett, S Mendelson
Journal of Machine Learning Research 3 (Nov), 463-482, 2002
19132002
Neural network learning: Theoretical foundations
M Anthony, PL Bartlett
cambridge university press, 2009
17602009
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
1549*1998
A framework for learning predictive structures from multiple tasks and unlabeled data.
RK Ando, T Zhang, P Bartlett
Journal of Machine Learning Research 6 (11), 2005
14542005
Convexity, classification, and risk bounds
PL Bartlett, MI Jordan, JD McAuliffe
Journal of the American Statistical Association 101 (473), 138-156, 2006
12572006
Boosting algorithms as gradient descent in function space
L Mason, J Baxter, P Bartlett, M Frean
Nips 11, 512-518, 1999
9591999
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
8682011
Infinite-horizon policy-gradient estimation
J Baxter, PL Bartlett
Journal of Artificial Intelligence Research 15, 319-350, 2001
8472001
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
6561998
Local rademacher complexities
PL Bartlett, O Bousquet, S Mendelson
The Annals of Statistics 33 (4), 1497-1537, 2005
6322005
Spectrally-normalized margin bounds for neural networks
P Bartlett, DJ Foster, M Telgarsky
arXiv preprint arXiv:1706.08498, 2017
5002017
Learning the Kernel Function via Regularization.
CA Micchelli, M Pontil, P Bartlett
Journal of machine learning research 6 (7), 2005
4602005
Sparse greedy Gaussian process regression
AJ Smola, PL Bartlett
Advances in neural information processing systems, 619-625, 2001
4312001
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
4292016
Model selection and error estimation
PL Bartlett, S Boucheron, G Lugosi
Machine Learning 48 (1), 85-113, 2002
4042002
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
3941999
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