excess risk

Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$ NeurIPS 2021 Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel NeurIPS 2021 Stochastic Online Optimization using Kalman Recursion JMLR 2021 Surrogate Regret Bounds for Polyhedral Losses NeurIPS 2021 A Statistical Learning Approach to Modal Regression JMLR 2020 In search of robust measures of generalization NeurIPS 2020 Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification ICML 2020 Statistical Learning with a Nuisance Component (Extended Abstract) IJCAI 2020 The Implicit Regularization of Stochastic Gradient Flow for Least Squares ICML 2020 The Statistical Complexity of Early-Stopped Mirror Descent NeurIPS 2020 A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes ALT 2019 Distribution-Dependent Analysis of Gibbs-ERM Principle COLT 2019 PAC-Bayes Un-Expected Bernstein Inequality NeurIPS 2019 Rates of Convergence for Large-scale Nearest Neighbor Classification NeurIPS 2019 Statistical Learning with a Nuisance Component COLT 2019 Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the $O(1/T)$ Convergence Rate COLT 2019 PAC-Bayes Tree: weighted subtrees with guarantees NeurIPS 2018 Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models NeurIPS 2017 Fast rates with high probability in exp-concave statistical learning AISTATS 2017 Hypothesis Transfer Learning via Transformation Functions NeurIPS 2017 Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015 Lower and Upper Bounds on the Generalization of Stochastic Exponentially Concave Optimization COLT 2015 Nearly Optimal Private LASSO NeurIPS 2015 From Stochastic Mixability to Fast Rates NeurIPS 2014 Multitask learning meets tensor factorization: task imputation via convex optimization NeurIPS 2014