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
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 9, 8, 1999
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
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
L Mason, J Baxter, P Bartlett, M Frean
Advances in neural information processing systems 12, 1999
Byzantine-robust distributed learning: Towards optimal statistical rates
D Yin, Y Chen, R Kannan, P Bartlett
International conference on machine learning, 5650-5659, 2018
Spectrally-normalized margin bounds for neural networks
PL Bartlett, DJ Foster, MJ Telgarsky
Advances in neural information processing systems 30, 2017
Infinite-horizon policy-gradient estimation
J Baxter, PL Bartlett
journal of artificial intelligence research 15, 319-350, 2001
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
Local rademacher complexities
PL Bartlett, O Bousquet, S Mendelson
Benign overfitting in linear regression
PL Bartlett, PM Long, G Lugosi, A Tsigler
Proceedings of the National Academy of Sciences 117 (48), 30063-30070, 2020
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
Learning Rates for Q-learning.
E Even-Dar, Y Mansour, P Bartlett
Journal of machine learning Research 5 (1), 2003
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
PL Bartlett, N Harvey, C Liaw, A Mehrabian
Journal of Machine Learning Research 20 (63), 1-17, 2019
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning.
E Greensmith, PL Bartlett, J Baxter
Journal of Machine Learning Research 5 (9), 2004
Classification with a Reject Option using a Hinge Loss.
PL Bartlett, MH Wegkamp
Journal of Machine Learning Research 9 (8), 2008
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