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Learning Theory

5,334 papers

Papers per year

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Papers

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning NIPS 2011
On U-processes and clustering performance NIPS 2011
On Learning Discrete Graphical Models using Greedy Methods NIPS 2011
On the Universality of Online Mirror Descent NIPS 2011
Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning NIPS 2011
Speedy Q-Learning NIPS 2011
Greedy Model Averaging NIPS 2011
Statistical Tests for Optimization Efficiency NIPS 2011
Multi-Bandit Best Arm Identification NIPS 2011
Minimax Localization of Structural Information in Large Noisy Matrices NIPS 2011
Noise Thresholds for Spectral Clustering NIPS 2011
From Bandits to Experts: On the Value of Side-Observations NIPS 2011
Learning with the weighted trace-norm under arbitrary sampling distributions NIPS 2011
Greedy Algorithms for Structurally Constrained High Dimensional Problems NIPS 2011
Learning to Locate Relative Outliers ACML 2011
Contextual Bandit Algorithms with Supervised Learning Guarantees AISTATS 2011
Discussion of “Contextual Bandit Algorithms with Supervised Learning Guarantees” AISTATS 2011
Contextual Bandits with Linear Payoff Functions AISTATS 2011
Improved Loss Bounds For Multiple Kernel Learning AISTATS 2011
Adaptive Bandits: Towards the best history-dependent strategy AISTATS 2011
On NDCG Consistency of Listwise Ranking Methods AISTATS 2011
Generalization Bound for Infinitely Divisible Empirical Process AISTATS 2011
Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning AISTATS 2011
Regret Bounds for the Adaptive Control of Linear Quadratic Systems COLT 2011
Blackwell Approachability and No-Regret Learning are Equivalent COLT 2011