NIPS Proceedings
^{β}
Books
Peter L. Bartlett
19 Papers
Information-theoretic lower bounds on the oracle complexity of convex optimization
(2009)
Adaptive Online Gradient Descent
(2007)
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
(2007)
AdaBoost is Consistent
(2006)
Sample Complexity of Policy Search with Known Dynamics
(2006)
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
(2006)
Exponentiated Gradient Algorithms for Large-margin Structured Classification
(2004)
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates
(2003)
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
(2001)
Sparse Greedy Gaussian Process Regression
(2000)
Boosting Algorithms as Gradient Descent
(1999)
Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks
(1998)
Direct Optimization of Margins Improves Generalization in Combined Classifiers
(1998)
Shrinking the Tube: A New Support Vector Regression Algorithm
(1998)
Generalization in Decision Trees and DNF: Does Size Matter?
(1997)
The Canonical Distortion Measure in Feature Space and 1-NN Classification
(1997)
For Valid Generalization the Size of the Weights is More Important than the Size of the Network
(1996)
Examples of learning curves from a modified VC-formalism
(1995)
Splines, Rational Functions and Neural Networks
(1991)
1 Book
Advances in Neural Information Processing Systems 24
(2011)