surrogate loss

Learning Halfspaces with Massart Noise Under Structured Distributions COLT 2020 Minimax Classification with 0-1 Loss and Performance Guarantees NeurIPS 2020 Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? NeurIPS 2020 On the consistency of top-k surrogate losses ICML 2020 Stochastic AUC Maximization with Deep Neural Networks ICLR 2020 An Embedding Framework for Consistent Polyhedral Surrogates NeurIPS 2019 Stochastic Negative Mining for Learning with Large Output Spaces AISTATS 2019 Structured Prediction with Projection Oracles NeurIPS 2019 Contextual bandits with surrogate losses: Margin bounds and efficient algorithms NeurIPS 2018 Cost-Sensitive Learning with Noisy Labels JMLR 2018 The Many Faces of Exponential Weights in Online Learning COLT 2018 The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning NeurIPS 2018 Understanding the Loss Surface of Neural Networks for Binary Classification ICML 2018 Adversarial Surrogate Losses for Ordinal Regression NeurIPS 2017 Efficient Online Multiclass Prediction on Graphs via Surrogate Losses AISTATS 2017 On the Consistency of Ordinal Regression Methods JMLR 2017 A Consistent Regularization Approach for Structured Prediction NeurIPS 2016 Convex Calibration Dimension for Multiclass Loss Matrices JMLR 2016 Surrogate regret bounds for generalized classification performance metrics ACML 2015 Analysis of Learning from Positive and Unlabeled Data NeurIPS 2014 Consistent Binary Classification with Generalized Performance Metrics NeurIPS 2014 On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems COLT 2014 Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses NeurIPS 2013 Cost-sensitive Multiclass Classification Risk Bounds ICML 2013 Learning with Noisy Labels NeurIPS 2013