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All publications by Peter L. Bartlett
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Accelerated Mirror Descent in Continuous and Discrete Time
Walid Krichene, Alexandre Bayen and Peter L. Bartlett
Advances in Neural Information Processing Systems 28, 2015


Minimax Time Series Prediction
Wouter M. Koolen, Alan Malek, Peter L. Bartlett and Yasin Abbasi
Advances in Neural Information Processing Systems 28, 2015


Large-Margin Convex Polytope Machine
Alex Kantchelian, Michael C. Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph and J. D. Tygar
Advances in Neural Information Processing Systems 27, 2014


Efficient Minimax Strategies for Square Loss Games
Wouter M. Koolen, Alan Malek and Peter L. Bartlett
Advances in Neural Information Processing Systems 27, 2014


Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior
Fares Hedayati and Peter L. Bartlett
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Implicit Online Learning
Brian Kulis and Peter L. Bartlett
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


Optimal Allocation Strategies for the Dark Pool Problem
Alekh Agarwal, Peter L. Bartlett and Max Dama
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Information-theoretic lower bounds on the oracle complexity of convex optimization
Alekh Agarwal, Martin J. Wainwright, Peter L. Bartlett and Pradeep K. Ravikumar
Advances in Neural Information Processing Systems 22, 2009


Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras and Peter L. Bartlett
Journal of Machine Learning Research, 2008


Classification with a Reject Option using a Hinge Loss
Peter L. Bartlett and Marten H. Wegkamp
Journal of Machine Learning Research, 2008


Online discovery of similarity mappings
Alexander Rakhlin, Jacob Abernethy and Peter L. Bartlett
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


On the Consistency of Multiclass Classification Methods
Ambuj Tewari and Peter L. Bartlett
Journal of Machine Learning Research, 2007


Adaptive Online Gradient Descent
Elad Hazan, Alexander Rakhlin and Peter L. Bartlett
Advances in Neural Information Processing Systems 20, 2007


Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
Ambuj Tewari and Peter L. Bartlett
Advances in Neural Information Processing Systems 20, 2007


AdaBoost is Consistent
Peter L. Bartlett and Mikhail Traskin
Journal of Machine Learning Research, 2007


Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
Peter L. Bartlett and Ambuj Tewari
Journal of Machine Learning Research, 2007


The Rademacher Complexity of Co-Regularized Kernel Classes
David S. Rosenberg and Peter L. Bartlett
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
Benjamin I. Rubinstein, Peter L. Bartlett and J. H. Rubinstein
Advances in Neural Information Processing Systems 19, 2006


Sample Complexity of Policy Search with Known Dynamics
Peter L. Bartlett and Ambuj Tewari
Advances in Neural Information Processing Systems 19, 2006


AdaBoost is Consistent
Peter L. Bartlett and Mikhail Traskin
Advances in Neural Information Processing Systems 19, 2006


Exponentiated Gradient Algorithms for Large-margin Structured Classification
Peter L. Bartlett, Michael Collins, Ben Taskar and David A. Mcallester
Advances in Neural Information Processing Systems 17, 2004


Learning the Kernel Matrix with Semidefinite Programming
Gert Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent E. Ghaoui and Michael I. Jordan
Journal of Machine Learning Research, 2004


Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Evan Greensmith, Peter L. Bartlett and Jonathan Baxter
Journal of Machine Learning Research, 2004


Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates
Peter L. Bartlett, Michael I. Jordan and Jon D. Mcauliffe
Advances in Neural Information Processing Systems 16, 2003


Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
Peter L. Bartlett and Shahar Mendelson
Journal of Machine Learning Research, 2002