← Optimization & Theory
Machine Learning › Optimization & Theory ›

Learning Theory

5312 directly classified papers

Papers per year

Papers

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
Finite-Time Bounds for Fitted Value Iteration JMLR 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
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning JMLR 2007
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results JMLR 2007
Dynamics and Generalization Ability of LVQ Algorithms JMLR 2007
Combining PAC-Bayesian and Generic Chaining Bounds JMLR 2007
Statistical Analysis of Semi-Supervised Regression NIPS 2007
Polynomial Identification in the Limit of Substitutable Context-free Languages JMLR 2007
Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions JMLR 2007
VC Theory of Large Margin Multi-Category Classifiers JMLR 2007
Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data JMLR 2007
AdaBoost is Consistent JMLR 2007
Unlabeled Compression Schemes for Maximum Classes 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