heavy tail

Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent JMLR 2026 Flexible Tails for Normalizing Flows ICML 2025 Non-Asymptotic and Non-Lipschitzian Bounds on Optimal Values in Stochastic Optimization Under Heavy Tails ICML 2025 Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails JMLR 2024 Generalization in Kernel Regression Under Realistic Assumptions ICML 2024 Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent NeurIPS 2023 Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility JMLR 2023 On the Overlooked Structure of Stochastic Gradients NeurIPS 2023 High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails ICML 2022 Bayesian neural network unit priors and generalized Weibull-tail property ACML 2021 Consistent estimation of small masses in feature sampling JMLR 2021 Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks NeurIPS 2021 High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails NeurIPS 2021 Open Problem: Restricted Eigenvalue Condition for Heavy Tailed Designs COLT 2015 Rare Probability Estimation under Regularly Varying Heavy Tails COLT 2012