Loss Functions

A Characterization of Scoring Rules for Linear Properties COLT 2012 A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems COLT 2012 A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss JMLR 2012 Classification Calibration Dimension for General Multiclass Losses NeurIPS 2012 Learning from the Wisdom of Crowds by Minimax Entropy NeurIPS 2012 Learning Low-order Models for Enforcing High-order Statistics AISTATS 2012 On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking NeurIPS 2012 Persistent Homology for Learning Densities with Bounded Support NeurIPS 2012 Proper losses for learning from partial labels NeurIPS 2012 Selective Labeling via Error Bound Minimization NeurIPS 2012 A concave regularization technique for sparse mixture models NeurIPS 2011 Approximate inference for the loss-calibrated Bayesian AISTATS 2011 Composite Multiclass Losses NeurIPS 2011 Efficient variable selection in support vector machines via the alternating direction method of multipliers AISTATS 2011 Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss NeurIPS 2011 Mixability is Bayes Risk Curvature Relative to Log Loss COLT 2011 Multiclass Boosting: Theory and Algorithms NeurIPS 2011 Neyman-Pearson Classification, Convexity and Stochastic Constraints JMLR 2011 Semi-supervised Learning by Higher Order Regularization AISTATS 2011 Trace Lasso: a trace norm regularization for correlated designs NeurIPS 2011 Variance Penalizing AdaBoost NeurIPS 2011 Classification Methods with Reject Option Based on Convex Risk Minimization JMLR 2010 Convexity of Proper Composite Binary Losses AISTATS 2010 Empirical Bernstein Boosting AISTATS 2010 Maximum Relative Margin and Data-Dependent Regularization JMLR 2010