excess risk bound

Orthogonal Statistical Learning with Self-Concordant Loss COLT 2022 Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks NeurIPS 2022 When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS 2022 Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods ICLR 2021 Benign Overfitting of Constant-Stepsize SGD for Linear Regression COLT 2021 Consistency and Finite Sample Behavior of Binary Class Probability Estimation AAAI 2021 Surrogate Regret Bounds for Polyhedral Losses NeurIPS 2021 Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes JMLR 2020 A tight excess risk bound via a unified PAC-Bayesian–Rademacher–Shtarkov–MDL complexity ALT 2019 Classification using margin pursuit AISTATS 2019 Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction ICML 2019 On Symmetric Losses for Learning from Corrupted Labels ICML 2019 Connecting Optimization and Regularization Paths NeurIPS 2018 Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms NeurIPS 2018 Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning JMLR 2018 On the Consistency of Ordinal Regression Methods JMLR 2017 Efficient Private Empirical Risk Minimization for High-dimensional Learning ICML 2016 Localized Complexities for Transductive Learning COLT 2014 Excess risk bounds for multitask learning with trace norm regularization COLT 2013 Loss-Proportional Subsampling for Subsequent ERM ICML 2013 The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning NeurIPS 2011 Rademacher Complexities and Bounding the Excess Risk in Active Learning JMLR 2010 Smoothness, Low Noise and Fast Rates NeurIPS 2010 Compressed Least-Squares Regression NeurIPS 2009