empirical process

Kernel-based Distributed Learning JMLR 2026 Towards Improved Risk Bounds for Transductive Learning IJCAI 2025 Correlated Binomial Process COLT 2024 Lower Complexity Adaptation for Empirical Entropic Optimal Transport JMLR 2024 Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes ALT 2023 Network Regression with Graph Laplacians JMLR 2022 Neural Estimation of Statistical Divergences JMLR 2022 Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification JMLR 2022 Experimental Design for Regret Minimization in Linear Bandits AISTATS 2021 Functions with average smoothness: structure, algorithms, and learning COLT 2021 Majorizing Measures, Sequential Complexities, and Online Learning COLT 2021 An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits NeurIPS 2020 Estimating weighted areas under the ROC curve NeurIPS 2020 Uniform concentration and symmetrization for weak interactions COLT 2019 Concentration inequalities for empirical processes of linear time series JMLR 2018 How Many Samples are Needed to Estimate a Convolutional Neural Network? NeurIPS 2018 Plug-in Estimators for Conditional Expectations and Probabilities AISTATS 2018 Convergence of Gradient EM on Multi-component Mixture of Gaussians NeurIPS 2017 Linear Convergence of Stochastic Frank Wolfe Variants AISTATS 2017 A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries JMLR 2016 High Dimensional Structured Superposition Models NeurIPS 2016 A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models AISTATS 2014 Localized Complexities for Transductive Learning COLT 2014 Generalization Bound for Infinitely Divisible Empirical Process AISTATS 2011