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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
P Bartlett, Y Freund, WS Lee, RE Schapire
The annals of statistics 26 (5), 1651-1686, 1998
36311998
New support vector algorithms
B Schölkopf, AJ Smola, RC Williamson, PL Bartlett
Neural computation 12 (5), 1207-1245, 2000
35802000
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
29732004
Rademacher and Gaussian complexities: Risk bounds and structural results
PL Bartlett, S Mendelson
Journal of Machine Learning Research 3 (Nov), 463-482, 2002
22952002
Neural network learning: Theoretical foundations
M Anthony, PL Bartlett, PL Bartlett
cambridge university press, 1999
20661999
For valid generalization the size of the weights is more important than the size of the network
P Bartlett
Advances in neural information processing systems 9, 1996
16751996
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
15492005
Convexity, classification, and risk bounds
PL Bartlett, MI Jordan, JD McAuliffe
Journal of the American Statistical Association 101 (473), 138-156, 2006
14132006
Boosting algorithms as gradient descent
L Mason, J Baxter, P Bartlett, M Frean
Advances in neural information processing systems 12, 1999
12301999
Infinite-horizon policy-gradient estimation
J Baxter, PL Bartlett
Journal of Artificial Intelligence Research 15, 319-350, 2001
9352001
Spectrally-normalized margin bounds for neural networks
PL Bartlett, DJ Foster, MJ Telgarsky
Advances in neural information processing systems 30, 2017
8032017
Local rademacher complexities
PL Bartlett, O Bousquet, S Mendelson
The Annals of Statistics 33 (4), 1497-1537, 2005
7412005
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
7252016
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
6901998
Learning Rates for Q-learning.
E Even-Dar, Y Mansour, P Bartlett
Journal of machine learning Research 5 (1), 2003
4882003
Byzantine-robust distributed learning: Towards optimal statistical rates
D Yin, Y Chen, R Kannan, P Bartlett
International Conference on Machine Learning, 5650-5659, 2018
4852018
Learning the Kernel Function via Regularization.
CA Micchelli, M Pontil, P Bartlett
Journal of machine learning research 6 (7), 2005
4842005
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning.
E Greensmith, PL Bartlett, J Baxter
Journal of Machine Learning Research 5 (9), 2004
4672004
Sparse greedy Gaussian process regression
A Smola, P Bartlett
Advances in neural information processing systems 13, 2000
4632000
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
4321999
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