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Learning Theory

5312 directly classified papers

Papers per year

Papers

Rademacher Complexity Bounds for Non-I.I.D. Processes NIPS 2008
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice NIPS 2008
Domain Adaptation with Multiple Sources NIPS 2008
Linear Classification and Selective Sampling Under Low Noise Conditions NIPS 2008
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization NIPS 2008
A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning NIPS 2008
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning NIPS 2008
Unlabeled data: Now it helps, now it doesn't NIPS 2008
Consistency of the Group Lasso and Multiple Kernel Learning JMLR 2008
Ranking Categorical Features Using Generalization Properties JMLR 2008
From External to Internal Regret JMLR 2007
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs NIPS 2007
Convex Learning with Invariances NIPS 2007
Adaptive Online Gradient Descent NIPS 2007
Bundle Methods for Machine Learning NIPS 2007
Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations NIPS 2007
Selecting Observations against Adversarial Objectives NIPS 2007
Progressive mixture rules are deviation suboptimal NIPS 2007
Separating Models of Learning from Correlated and Uncorrelated Data JMLR 2007
Learnability of Gaussians with Flexible Variances JMLR 2007
Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria NIPS 2007
Boosting the Area under the ROC Curve NIPS 2007
The Value of Labeled and Unlabeled Examples when the Model is Imperfect NIPS 2007
Regret Minimization in Games with Incomplete Information NIPS 2007
The Tradeoffs of Large Scale Learning NIPS 2007