Classification

Learning from Partial Labels JMLR 2011 Learning from positive and unlabeled examples by enforcing statistical significance AISTATS 2011 Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint NeurIPS 2011 Learning large-margin halfspaces with more malicious noise NeurIPS 2011 Learning Rules from Incomplete Examples via Implicit Mention Models ACML 2011 Learning to Locate Relative Outliers ACML 2011 Linear-Time Estimators for Propensity Scores AISTATS 2011 LPmade: Link Prediction Made Easy JMLR 2011 Matrix Completion for Multi-label Image Classification NeurIPS 2011 Maximum Margin Multi-Instance Learning NeurIPS 2011 Maximum Margin Multi-Label Structured Prediction NeurIPS 2011 Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers ACML 2011 Mixability is Bayes Risk Curvature Relative to Log Loss COLT 2011 MSVMpack: A Multi-Class Support Vector Machine Package JMLR 2011 MULAN: A Java Library for Multi-Label Learning JMLR 2011 Multiclass Boosting: Theory and Algorithms NeurIPS 2011 Multiclass Learnability and the ERM principle COLT 2011 Multi-label Active Learning with Auxiliary Learner ACML 2011 Multi-label Classification with Error-correcting Codes ACML 2011 Multi-Label Output Codes using Canonical Correlation Analysis AISTATS 2011 Multiple Instance Learning on Structured Data NeurIPS 2011 Multiple Kernel Learning Algorithms JMLR 2011 Multitask Sparsity via Maximum Entropy Discrimination JMLR 2011 Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction NeurIPS 2011 Neyman-Pearson Classification, Convexity and Stochastic Constraints JMLR 2011