bayes optimal classifier

Bayes-Optimal Fair Classification with Multiple Sensitive Features AAAI 2026 Convergence of Vector Quantization–Based Classifiers to the Bayes Optimal Classifier with Applications to Hybrid System Identification L4DC 2026 A Two-Stage Active Learning Algorithm for k-Nearest Neighbors ICML 2023 FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning ICML 2023 Progressive Purification for Instance-Dependent Partial Label Learning ICML 2023 Fair Bayes-Optimal Classifiers Under Predictive Parity NeurIPS 2022 Improved Algorithms for Neural Active Learning NeurIPS 2022 Knowledge Distillation: Bad Models Can Be Good Role Models NeurIPS 2022 Optimal Binary Classification Beyond Accuracy NeurIPS 2022 Robustness to Label Noise Depends on the Shape of the Noise Distribution NeurIPS 2022 Self-training Converts Weak Learners to Strong Learners in Mixture Models AISTATS 2022 Consistent Non-Parametric Methods for Maximizing Robustness NeurIPS 2021 On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective CVPR 2021 On the Existence of The Adversarial Bayes Classifier NeurIPS 2021 Unintended Selection: Persistent Qualification Rate Disparities and Interventions NeurIPS 2021 Convex Calibrated Surrogates for the Multi-Label F-Measure ICML 2020 Fairness with Overlapping Groups; a Probabilistic Perspective NeurIPS 2020 Learning with Bounded Instance and Label-dependent Label Noise ICML 2020 Analyzing the Robustness of Nearest Neighbors to Adversarial Examples ICML 2018 Binary Classification with Karmic, Threshold-Quasi-Concave Metrics ICML 2018 Convex Calibrated Surrogates for Hierarchical Classification ICML 2015 On the Bayes-Optimality of F-Measure Maximizers JMLR 2014 On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures NeurIPS 2014 Consistency versus Realizable H-Consistency for Multiclass Classification ICML 2013 The Minimum Error Minimax Probability Machine JMLR 2004