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Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
We study stochastic convex optimization under infinite noise variance.
Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
Consider a panel data setting where repeated observations on individuals are available.
Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes
Existing work mostly focuses on asymptotic dependence, where the probability of observing a large value in one of the variables is of the same order as observing a large value in all variables simultaneously.
Statistics Theory Methodology Statistics Theory 62G32 (Primary) 62F12, 62G20, 62G30 (Secondary)
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Structured non-convex learning problems, for which critical points have favorable statistical properties, arise frequently in statistical machine learning.
