Learning Theory

Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms JMLR 2011 k-NN Regression Adapts to Local Intrinsic Dimension NeurIPS 2011 Learning Eigenvectors for Free NeurIPS 2011 Learning large-margin halfspaces with more malicious noise NeurIPS 2011 Learning to Locate Relative Outliers ACML 2011 Learning with the weighted trace-norm under arbitrary sampling distributions NeurIPS 2011 Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas COLT 2011 Lower Bounds for Passive and Active Learning NeurIPS 2011 Maximum Likelihood vs. Sequential Normalized Maximum Likelihood in On-line Density Estimation COLT 2011 Maximum Margin Multi-Label Structured Prediction NeurIPS 2011 Minimax Localization of Structural Information in Large Noisy Matrices NeurIPS 2011 Missing Information Impediments to Learnability COLT 2011 Models of Cooperative Teaching and Learning JMLR 2011 Multi-armed bandits on implicit metric spaces NeurIPS 2011 Multi-Bandit Best Arm Identification NeurIPS 2011 Multiclass Boosting: Theory and Algorithms NeurIPS 2011 Multiclass Learnability and the ERM principle COLT 2011 Noise Thresholds for Spectral Clustering NeurIPS 2011 Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NeurIPS 2011 On Learning Discrete Graphical Models using Greedy Methods NeurIPS 2011 Online Learning: Beyond Regret COLT 2011 Online Learning: Stochastic, Constrained, and Smoothed Adversaries NeurIPS 2011 On NDCG Consistency of Listwise Ranking Methods AISTATS 2011 On the Consistency of Multi-Label Learning COLT 2011 On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem JMLR 2011