Peter Bartlett
Peter Bartlett
Professor, EECS and Statistics, UC Berkeley
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Citata da
Citata da
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
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, PL Bartlett
cambridge university press, 1999
Regularization networks and support vector machines
T Evgeniou, M Pontil, T Poggio
Advances in computational mathematics 13 (1), 1-50, 2000
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
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
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 in function space
L Mason, J Baxter, P Bartlett, M Frean
Proc. NIPS 12, 512-518, 1999
Infinite-horizon policy-gradient estimation
J Baxter, PL Bartlett
Journal of Artificial Intelligence Research 15, 319-350, 2001
Spectrally-normalized margin bounds for neural networks
P Bartlett, DJ Foster, M Telgarsky
arXiv preprint arXiv:1706.08498, 2017
Local rademacher complexities
PL Bartlett, O Bousquet, S Mendelson
The Annals of Statistics 33 (4), 1497-1537, 2005
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
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
Learning the Kernel Function via Regularization.
CA Micchelli, M Pontil, P Bartlett
Journal of machine learning research 6 (7), 2005
Sparse greedy Gaussian process regression
AJ Smola, PL Bartlett
Advances in neural information processing systems, 619-625, 2001
Learning Rates for Q-learning.
E Even-Dar, Y Mansour, P Bartlett
Journal of machine learning Research 5 (1), 2003
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning.
E Greensmith, PL Bartlett, J Baxter
Journal of Machine Learning Research 5 (9), 2004
Model selection and error estimation
PL Bartlett, S Boucheron, G Lugosi
Machine Learning 48 (1), 85-113, 2002
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